Sentiment Analysis Using Machine Learning Algorithms
Corresponding Dr. R. Udayakumar,
Professor, Department of Information Technology, Bharath Institute of Higher Education and Research, Selaiyur, Chennai-600073.
Email-id: [email protected] Muhammad Abul Kalam
Assistant Professor, Department of Computer Science and Engineering, CMR Institute of Technology, Kandlakoya, Medchal, Hyderabad-501401,
Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai-600073.
Email-id: [email protected]
Article Info
Page Number: 1186 – 1200 Publication Issue:
Vol. 71 No. 3s2 (2022)
Article History
Article Received: 28 April 2022 Revised: 15 May 2022
Accepted: 20 June 2022 Publication: 21 July 2022
Abstract
The sentiments are a basic mental process that reflects the individual characteristics of the surrounding reality and the internal state of the body with the direct impact of stimuli on the senses. There is a lot of different available between perception and sentiment. The emotions reflect personal characteristics, but not events and objects as perception. Thus, external phenomena act on our senses and cause a subjective effect in the form of emotions without any counter-action of the object related to the perceived impact. In this paper, we will discuss sentiment analysis on the basis of the smart machine learning model. All organisms with a nervous system are endowed with the ability to feel from birth. Only man and higher animals can perceive the world in the form of images, which develops and enhances their life experience. The proposed model computes the human activities as per the sentimental behaviour of the human and achieved 86.73% of emotions, 95.66% of emotional adoptions, 94.56% of consciousness formation, 95.50% of objectivity and 94.40% of cognitive disorders.
Keywords — Sentiment Analysis, Mental Process, Perceptions, Emotions, Internal Environments, Counter Action, Machine Learning
1. Introduction
The cognitive process of the outside world is one of the areas studied by psychology. Modern science is in this case the heir and successor of ancient philosophy rooted in antiquity. To date, psychologists have come to a consensus on the basic concepts that describe emotion [1].
This process is very complex and has not yet been fully explored, but by learning a few concepts a general idea about it can be developed for you. Therefore, there are two standard terms to describe this function of the human brain: perception and perception [2].
Modern Western psychology suggests that human interaction with the world begins with emotions. The latter is a simple reflection in the human mind of various phenomena of the external world, perceived by the five senses [3]. Emotions do not reveal the whole image of the environment, but only aspects of direct contact with a person. The brain analyzes the information obtained from the emotions and creates the final representation of the external reality. Realization is the result of this. Unlike the basic elements of cognition, it is a complex mixing process that incorporates individual emotions into structural elements [4-7].
Thus, when interacting with the fruit, the individual has five completely independent senses [8]. But complex mental mechanisms combine all the information from them and provide consciousness through a single image, an idea of an apple, i.e. a complete image of reality [9]. This is the overall view of the environment, which is a concept of the world. But there is a subtlety when the feeling is synonymous with the concept [10-11].
The Convolution Neural Network module is large, including the currently calculated sentimental module and its data and is used to analyze sentimental data already stored in the database. Psychological theories give an important place to basic processes such as emotions.
They are the structural foundation of a whole set of highly complex mental processes [12].
Not only is full consciousness impossible without sensations, but thought cannot function without the experience of interaction with the external objects they provide [13].
Cognition is, as we have found, the total complexity of emotions processed by the brain [14].
Analyzers consist of a tripartite system consisting of receivers, conductors, and a hub.
Receptors are nerve endings that communicate directly with external objects and sensitize information through irritation [15].
Accordingly, they are located in the sensory organs - eyes, ears, tongue, nasal cavity, and skin. Such receptors are called external receptors, i.e., receptors that are externally directed to the external world. They are the basis for the primary perceptions of the surrounding reality [16]. But along with them, there is another group of receptors that target a person’s inner senses - hunger, thirst, etc. These nerve endings are called intero receptors [17]. The conductors, or pathways, that appear in receptors and end at nerve centres are called nerve fibres. The function of these connections is to transmit neural signals from the receptors to the centre of the analyzer [18]. The centre of the analyst is the brain. More precisely, its various parts are responsible for the part of the sense organs entrusted to them. Some parts of the brain are responsible for visual perception, others for tactile sensation, and many more.
The centre of the analyzer receives a signal from the receiver through the conductors, converting it into a specific sensation that a person perceives [19].
These are aspects of perception of the outside world - in fact; we cannot directly taste or smell. Our brain re-creates and reconstructs emotions based on data received from receptors.
The whole panorama of different senses exists only in the human head. As already mentioned, there are a total of five sense organs per person [20]. However, according to modern psychology, the properties of consciousness are not five, but six senses, as one would expect. The truth is, that motor skills are not a sensory element that researchers classify as a
source of basic knowledge [21]. This property of hers adds a sixth sense called kinesthetic to the general treasury of emotions. There is no consensus among scientists as to which sensory organs are, accordingly, which sense is most important, i.e., carrying the most valuable information to the analytical centre of the brain, which creates the ultimate sensation [22]. In psychology, more precisely in its mainstreams, the leading role today is given to vision. Most of the information that makes up the vision (up to 80%) is believed to be visual communication. Like it or not, visual activity in any case is the most important source of information about the outside world [23]. Her sensory organs are a pair of eyes, which sense information from light vibrations at the physical level [24]. The proper functioning of the eyes allows us to sense the photon waves in the colour spectrum, which allows the brain to create many colours that make the world colourful in our minds. In human psychology, experts distinguish many types and emotions simultaneously. Currently, it is a more or less established and universal organization that is universally accepted. Like elsewhere, the development of consciousness is complicated by simplicity [25]. Based on one of the simplest types of emotions. It could be listening to music or smelling a flower. In these examples, feedback is generated by an analyst based on a stimulus. Multiple emotions are included in the reflection process, for example, when watching a movie or arranging a bouquet, the concept is of complex types. In addition, the concept of psychology is divided into several categories. This classification is based on the distinction between the types of perceived objects [26]. Therefore, experts distinguish between time perception, space perception, motion perception and human perception. The latter is scientifically called a social opinion. The notion of time is based on changes in the internal processes of the human psyche, so it is often subjective. The concept of space gives an idea of the shape, size and arrangement of objects in three-dimensional reality [27-32].
The main contribution of this paper is to identify the sentimental analysis with the enhanced convolution neural networks model. Moving objects on the coordinate axis create a sense of motion. The relative senses the movement of an object relative to other objects.
Inappropriate, on the contrary, isolates the object from outsiders. The comparison of sentimental with a set of enhanced lesions will help to determine which block sentiments are based on their type. And saves its special features by accurately checking what changes are present in the size of the previous sentiments
.
2. Literature Review
It should be noted that the colours are colourful, i.e., form the observed colour spectrum of the rainbow and coloured against them. There are only three of them - black, white and gray.
Following visual information, the ability to recognize sound plays a very important role in human life [4]. The latter is not the only important means of communication. Sound waves perceived by auditory receptors are divided into two groups according to the nature of the sensation. The first involves noise sensations, i.e. sounds that have no rhythmic structure in the vibrations of the sound wave [6]. Rather, rhythmically organized waves are called musical sensations. Most people's life activities involve considerable movement - many daily activities that cannot be done without involving walking, typing, dressing and motor activity.
Hence the importance of clarity of motor senses for life, because without them it would be
very difficult to even bring a spoon into your mouth [8]. These motor sensations, as mentioned above, are created not by the sensory organs, but by nerve endings that are distributed throughout the body. Tactile sensations are also important for people to interact with the outside world, and they also provide a deeper sense of a person by person. This is especially noticeable in a sexual environment, but also in raising children and other types of relationships [9]. For example, it is enough to recall the tradition of shaking hands. In other words, the sense of touch is of direct importance to the continuity of organisms (and, therefore, to the protection of organisms), and to the development of society as a whole.
Some, i.e. deaf people, i.e., those who are unable to see and hear, generally use tactile sensations as the only form of communication with others [10]. Generally, pre-processing is the application of signal processing techniques to the domain of images - two-dimensional signals such as photos or video. Image processing usually involves filtering or enhancing an image using a variety of functions in addition to other techniques for extracting information from sentimental inputs.
In general, psychologists distinguish two types of tactile sensation: tactile and temperature.
The latter is responsible for recognizing heat and cold, and the former covers the complex rest of the various senses associated with touch. In humans, the sense of taste is well developed and the sense of smell is stronger than the sense of smell. In addition to the tongue, part of the soft palate belongs to the sense organs of this sense [13]. The sense of taste consists of four components: bitter, sweet, acidic and salty. A specific part of the tongue is responsible for each of them, and the final combination of four factors produces all kinds of tastes familiar to man. Features of human consciousness can sometimes group many basic emotions. In psychology, this phenomenon is referred to as "synesthesia". Often, a similar relationship occurs between the visual and auditory senses [15]. A person experiences synesthesia as a constant supplement between shadows and sounds. Another variant of synesthesia, though rare, is a set of visual sensations with olfactory sensation. This type of patch gives different colour shades their scent. A similar phenomenon develops in individuals associated with the sense of smell, for example, somnambulists or perfumes [17].
3. Proposed Model
The sentiments act as channels through which we connect with the outside world. This is because the required level of awareness is maintained due to energy perceptions. Education is closely related to cognitive functioning and is because the arrival of emotions is necessary for normal mental development, which can be very dangerous if emotions do not come at critical times in life (favourable periods for the development of a particular mental activity). The range of human emotions is strongly related to lifestyle and body condition. The proposed model functionalities are shown in the following fig 1.
Proposed model classifications:
• Extrinsic - external, communicative and distant senses
• Interceptive- Irritation from the internal environment, sometimes we do not even know
• Proprioceptive- Feelings from our musculoskeletal system
Fig 1: Proposed model classifications of sentiments
Sentiment Categories: Visual Ear, Taste, Tactile, Organic. Forms of sensation:
• One minute (low threshold) and maximum. (The upper threshold of emotions);
• the presence of the difference threshold;
• adaptation (which is a change in sensitivity analysis under the influence of an annoying prolonged exposure);
• Sensitivity (mutual influence of the stimulus of one receptor on the work of another).
The Data set based on the data are given first will be collected. That image block will then be classified based on the data collected. Its modules are still being upgraded to preprocessing it.
Then its colour block is divided into groups of variants. The segment is then classified into blocks and then its data is further classified under its various stages. Finally, its results are released. The ensemble learning technique, which is used for the categorization of hyperspectral images, makes use of seven machine learning algorithms that are piled on top of one another for the automated sentiments.
Fig 2: Proposed System model
Extrinsic Sentiments
Proprioceptive Centiments Interceptive
Centiments
Input Instructio
ns
Expanded Meanings
Data Gathering
Normalize d words-
NW
Remove error words
Feature Extraction
Classificatio n Module Input
Training Data
Knowledge Database
Traini ng Set
Test Set
Outp ut
 = sin (B(c)), (1) where
B(c)- Sentiment Identification function.
The unraveling sentiment is to assure the situation:
B(c) = Qdc + f = 0, (2)
c, Q ∈ ℝn, where f ∈ ℝ (3)
Where
x– feature extraction sentimental vector, Q- vector function weigtage
f- sentimental offset.
Emotions do not occur immediately after the onset of arousal: there is a very short period when the stimulus is active, but there are no sensations, and the sensation always has a spatial localization shown in fig 2.
Emotion adaptation: Emotion communication occurs when the presence of one emotion affects another. A special form of emotional interaction is synesthesia, ie. Under the influence of the emotions of one system, the emotions of another system appear shown in fig 3.
Fig 3: Proposed model training and test set functionalities
Proposed Sentiment Analysis
Training procedure Testing procedure
Sentimental input DB Fetching Input data’s
Training Preprocessing Testing Preprocessing
Training extraction Testing extraction
Training vector Testing classifier
Sentimental scale Output analysis details
Perception - This is a complete reflection on a person's mind, which directly affects his emotions, but not their characteristics, which occur during perception. Cognition is the reflection of a complex stimulus. There are four levels of cognitive activity: detection, discrimination, identification, and recognition. The first two are related to perception and the last - identifying actions.
Diagnosis: This is the initial stage of development of any emotional process. At this point, only the simple question of whether there is substance stimulation can be answered. The next perception function is a difference of perception. Its result is to create a perceptual image of the standard. At the same time, the development of cognitive activity continues in the order of assigning specific emotional content according to the properties of the given object and the task facing the object. When the cognitive figure is formed, an identification process can be performed. The core task C should convince the subsequent state:
C(qi, qj) = 𝛼(qi)d. 𝛼(qj) (4)
Where,
C- Semi Sentiment definite positive value
C(qi, qj) = C(qj, qi) which shows C is found to be sentimentally symmetric As an alternative way of expressing the equation, the following is provided:
∑𝑎𝑖=1∑𝑎𝑗=1𝑏𝑖𝑏𝑗𝐶(𝑞𝑖, 𝑞𝑗)≥ 0 (5)
Identification: Identifying directly perceived objects with the image stored in memory or identifying two objects perceived simultaneously. Recognition includes categorization (assigning an object to a specific type of object that was previously perceived) and extracting the corresponding quality from memory.
Algorithm: Sentimental Machine Learning Algorithm 1. Initialize the sentimental inputs
2. Evaluate the database as per the sentimental function 3. While (sentimental density < max. no.of reactions) 4. for every sentimental representative
5. Update the values of q 6. if (sentimental index < 1)
7. if (reaction values < maximum sentimental index) 8. Modify the search index of the image input 9. else if (reaction values > maximum sentimental index) 10. Perform a search index of the sentimental input 11. Update the values of q as per the new sentimental 12. Check the CNN values for sentimental instructions 13. Classify the sentiment type and provide the result 14. Display the results
15. Complete the process
Cognition is the system of cognitive activities. Cognition is divided into intentional (spontaneous) and intentional (spontaneous). This can be caused by the features of the objects
(their brightness, abnormality) surrounding the unplanned perception and the interaction of these objects with the individual interests. There is no pre-determined goal in the coincidence view. It has no optional function, which is why it is called arbitrary. For example, walking down the street, the noise of cars, people talking, and looking out the shop windows, we sense different smells. Deliberate perception is controlled by the task from the beginning - to perceive this or that object or event. Deliberate realization is looking at the electrical circuit of the machine being examined, listening to the report, and viewing the thematic exhibition.
It can be incorporated into any activity (in a labour process, in the performance of an educational task), which can function as an independent process
Attention- This is a spontaneous formal concept that is carried out with a specific, conscious goal with the help of voluntary attention. The clarity and orderliness are systematic conduct of the observer's work. People perceive the same information differently and subjectively depending on their interests, needs, and abilities. Name the dependent perception of feeling on the content of a person's mental life and the characteristics of his personality. Perceiving objects around us as relatively constant in shape, colour, and size Structural Perceptions - Perception is not a simple sum of emotions. When listening to music, we perceive and recognize a melody, not individual sounds.
Vulnerable hallucinations - arise under the influence of intense emotions, in a state of horror or high nervous tension, perceived as a skeleton swinging a twig out of a window. Verbal illusions - Misunderstanding of the meaning of words, and speech of others, instead of neutral speech, the patient hears speech of different content (usually threats, curses, accusations).
Causes of hallucinations shown in fig 4:
Fig 4: Proposed sentiment machine learning model for Verbal illusions
• Physiology.
• From focusing attention (from expecting concentration).
SMLM
imagination
tension of the senses
focusing attention Physiology
• From the tension of the senses. Emotional illusions
• Illusions of imagination.
Hallucinations are cognitive disorders in which a person sees, hears, and feels something that does not exist, viz. It is perception without feeling. Hallucinations are divided according to the sense organs: the auditory (pathological perception of words, conversations, individual sounds or sounds. The patient hears them calling).
• Scene (where the basics are - zigzags, sparks, fire; or objective, when images appear before the patient's eyes: unusual animals, scary figures or objects).
• Alpha factory (the patient feels unpleasant odours, rotting meat, burning, smoking, etc. They are sure to be fed with poison or spoiled food).
• Tactile (sensation of body touch, burning or cold, the patient sometimes feels he has been bitten or scratched).
• Viscera (senses of objects, animals, worms in your own body).
• Derealizationon (a disorder of perception in which objects around a patient, people, or animal are perceived as altered, associated with a sense of their alienation and unreality)
4. Results and discussions
The proposed Sentiment machine learning model (SMLA) was compared with the existing supervised learning and machine translation (SLMT), data mining machine learning algorithm (DMMLA), and Fuzzy bag-of-words model (FBWM) and Identity-Based Integrity Verification (IBIV). Here MATLAB was the tool used to compare the performance parameters.
4.1.Measurement of emotions
In psychology, there is a special category, the purpose of which involves the relationship between the strength of a stimulus and the brightness of an experienced feeling. This branch of science is called Psychophysics. Its task is to create an adequate system for calculating the limits of the senses and to create a corresponding scale of measurement shown in table 1.
Table 1: Measurements of emotions No of input
signals SLMT DMMLA FBWM IBIV SMLA
100 55.19 49.90 59.16 82.55 89.85
200 54.86 48.40 58.57 80.68 88.81
300 54.53 46.90 57.98 78.81 87.77
400 54.20 45.40 57.39 76.94 86.73
500 53.87 43.90 56.80 75.07 85.69
600 53.54 42.40 56.21 73.20 84.65
700 53.21 40.90 55.62 71.33 83.61
The proposed Sentiment machine learning approach performs the Psychologists propose to call the gateway to the appearance of a sensation, i.e., the minimal effect of a stimulus on the
disappearance of consciousness, the absolute lower threshold. Accordingly, there will be that amount of influence on the absolute upper threshold, and the feeling above it will disappear.
The frequencies below 16 Hz (infrared) and frequencies above 20 kHz (ultrasound) are examples of such limitations for the human ear.
4.2.Emotional adaptations
The Prolonged contact between stimuli and receptors initiates a process called sensory adaptation. In other words, the sensory organs may, by conventional expression, reduce their sensitivity to the point of completely ignoring the impact. This adaptation is called negative.
The proposed model comparisons are displayed in Table 2
Table 2: Measurements of emotional adaptations No of input
signals SLMT DMMLA FBWM IBIV SMLA
100 62.95 58.32 68.26 86.57 93.53
200 63.28 59.82 68.85 88.44 94.57
300 64.62 60.93 69.83 89.27 94.70
400 65.76 61.31 71.04 90.18 95.66
500 66.81 62.32 72.18 91.10 95.23
600 67.74 63.39 73.04 92.35 96.09
700 68.76 64.34 74.04 93.43 96.53
In the proposed model under the influence of prolonged contact with the stimulus, if the intensity of the senses increases, the adaptation is called positive. Most mobile adaptation in humans is observed for visual sensations and less flexibility - for auditory and painful experiences.
4.3.Formation of consciousness
It creates the sense of completeness of the senses described above. An important role in this process is played by memory, which allows a person to remember the experience gained in the process of interacting with the outside world. Thus, children's ideas begin to develop - playing, manipulating objects, crawling and grabbing everything in a row. The proposed model comparisons are displayed in Table 3
Table 3: Measurements of formation of consciousness No of input
signals SLMT DMMLA FBWM IBIV SMLA
100 59.95 54.25 64.68 85.33 89.91
200 60.29 55.66 65.42 86.20 91.46
300 60.63 57.07 66.16 87.07 93.01
400 60.97 58.48 66.90 87.94 94.56
500 61.31 59.89 67.64 88.81 96.11
600 61.65 61.30 68.38 89.68 97.66
700 61.99 62.71 69.12 90.55 99.21
The proposed ability to store memory summarizes all the information received in the form of experience and continues to enrich it throughout life. This allows the brain and consciousness to develop a holistic view of the outside world. It should be noted that perception is not just the sum of the senses stored in a bundle. It is a collection that allows us to perceive the whole world, in terms of many senses, without dividing it into different components of the mind.
4.4.Objectivity and consistency of feeling
Objectivity is the tangible nature of an object, i.e. its existence and objective existence in space and time. In contrast, psychologists isolate speculative, abstract concepts and genres that are not products and conceptual of the reflex process, but the benefit of thought or imagination. Therefore, only events with an objective characteristic can be perceived, this is called the objective start. The proposed model comparisons are displayed in table 4,
Table 4: Measurements of objectivity No of input
signals SLMT DMMLA FBWM IBIV SMLA
100 61.21 56.88 65.83 87.30 91.79
200 62.48 57.48 67.55 88.30 93.43
300 63.74 59.03 67.85 89.03 94.16
400 65.01 59.95 69.10 89.94 95.50
500 66.27 61.02 70.11 90.81 96.68
600 67.54 62.10 71.12 91.67 97.87
700 68.80 63.17 72.13 92.54 99.05
The proposed concept in psychology is characterized by consistency, that is, the ability of consciousness to preserve its essential properties for an object, regardless of the distance to a person. That is, the same object, for example, a large balloon, moves away from a person, yet can be consciously interpreted as a large balloon. This property of the soul helps to differentiate perspective and navigate adequately in space.
4.5.Cognitive disorders
The dysfunction of the senses causes failures in the coordination and communication of the object and the object. To some extent, it is possible to deliberately cause such disorders, using the features of the human psyche shown in table 5.
Table 5: Measurements of cognitive disorders No of input
signals SLMT DMMLA FBWM IBIV SMLA
100 68.92 61.29 58.71 78.08 94.19
200 70.21 62.04 63.33 81.48 94.29
300 69.96 62.01 63.33 81.12 94.36
400 69.83 61.19 62.86 79.93 94.40
500 69.91 61.10 62.66 80.06 94.44
600 69.90 60.97 62.40 80.08 94.47
700 69.55 60.55 61.77 79.65 94.49
The proposed model performs another way that affects the cognitive process is to accept psychotropic substances that cause hallucinations and vision. Consciousness is a simple mental process that arises from the direct impact of material stimuli on the sensory organs at a particular moment, reflecting the individual characteristics of objects and events in the surrounding world and the internal state of the body.
At a saturation point, the proposed sentiment machine learning model measured 86.73% of emotions, 95.66% of emotional adoptions, 94.56% of consciousness formation, 95.50% of objectivity and 94.40% of cognitive disorders. Hence the proposed model achieved better results when compared with the existing models.
5. Conclusion
It also feels a change in our own body: the position and movement of its parts, the position of the internal organs (pain, discomfort, etc.). With the help of the senses, we learn about the taste, colour, gravity, temperature of the objects around us, the properties of their surface (hardness, softness), and the sounds they produce. The senses are the primary source of our knowledge of the external world and our own body, the first level of knowledge. The proposed SMLA was compared with the existing SLMT, DMMLA, FBWM and Identity- Based Integrity Verification (IBIV). The proposed model is the direct connection of consciousness with the outside world, which transforms the energy of external irritation into the reality of consciousness. The concept develops based on emotions. But if consciousness is a reflection of the individual properties of objects and the phenomena of the material world, then perception always has an absolute character and reflects different properties in their totality. Cognition is the visual-visual reflection of objects, not objects that function in the senses at this time, but their individual properties and features.
References
[1] Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191.
[2] Balahur A, Turchi M (2014) Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Comput Speech Lang 28(1):56–
75. https://doi.org/10.1016/j.csl.2013.03.004
[3] Alkhabbas, F., Spalazzese, R., Cerioli, M., Leotta, M., & Reggio, G. (2020). On the deployment of IoT systems: An ındustrial survey. In 2020 IEEE International Conference on Software Architecture Companion (ICSA-C) (pp. 17–24).
[4] Li, Y. (2020). Practice of machine learning algorithm in data mining field. In 2020 International Conference on Advance in Ambient Computing and Intelligence (ICAACI) (pp. 56–59).
[5] Varun, B. N. ., S. . Vasavi, and S. . Basu. “Python Implementation of Intelligent System for Quality Control of Argo Floats Using Alpha Convex Hull”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 5, May 2022, pp. 60-64, doi:10.17762/ijritcc.v10i5.5554.
[6] Yu, Z., Wang, H., Lin, X., & Wang, M. (2016). Understanding short texts through semantic enrichment and hashing. IEEE Transactions on Knowledge and Data Engineering, 28(2), 566–579.
[7] Zhao, R., & Mao, K. (2017). Fuzzy bag-of-words model for document representation.
IEEE Transactions on Fuzzy Systems.
[8] Wang, W., Feng, F., He, X., Nie, L., & Chua, T.-S. (2021). Denoising implicit feedback for recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 373–381).
[9] Chaudhary, D. S. . (2022). Analysis of Concept of Big Data Process, Strategies, Adoption and Implementation. International Journal on Future Revolution in Computer Science
&Amp; Communication Engineering, 8(1), 05–08.
https://doi.org/10.17762/ijfrcsce.v8i1.2065
[10] Hong, M., & Wang, H. (2021). Research on customer opinion summarization using topic mining and deep neural network. Mathematics and Computers in Simulation, 185, 88–
114.
[11] Pradhan V, Vala J, Balani P (2016) A survey on sentiment analysis algorithms for opinion mining. Int J Comput Appl
[12] Erik C, Soujanya P, Alexander G, Mike T (2017) Sentiment analysis is a big suitcase.
IEEE
[13] Al-Shabi, M. A. (2020). Evaluating the performance of the most important lexicons used to sentiment analysis and opinions mining. IJCSNS, 20(1), 1.
[14] A. Chawla, “Phishing website analysis and detection using Machine Learning”, Int J Intell Syst Appl Eng, vol. 10, no. 1, pp. 10–16, Mar. 2022.
[15] Kumar, R. S., Devaraj, A. F. S., Rajeswari, M., Julie, E. G., Robinson, Y. H., &
Shanmuganathan, V. (2021). Exploration of sentiment analysis and legitimate artistry for opinion mining. Multimedia Tools and Applications, 1–16.
[16] Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015.
MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham.
[17] Mortazi, A., Karim, R., Rhode, K., Burt, J., & Bagci, U. (2017). Cardiacnet.:
Segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, & S. Duchesne
(Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017.
Springer.
[18] Caleffi, M., Cacciapuoti, A. S., & Bianchi, G. (2018, September). Quantum Internet:
From communication to distributed computing! In Proceedings of the 5th ACM International Conference on Nanoscale Computing and Communication (pp. 1–4).
[19] N. A. Farooqui, A. K. Mishra, and R. Mehra, “IOT based Automated Greenhouse Using Machine Learning Approach”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 226–231, May 2022.
[20] Sahu, I. K., & Nene, M. J. (2021, February). Identity-Based Integrity Verification (IBIV) protocol for cloud data storage. In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1–
6). IEEE.
[21] Abulaish, M., Kumari, N., Fazil, M., & Singh, B. (2019). A graph-theoretic embedding- based approach for rumor detection in twitter. In IEEE/WIC/ACM International Conference on Web Intelligence (pp. 466–470).
[22] M. Sutharsan, J. Logeshwaran, “Design Intelligence Data Gathering and Incident Response Model for Data Security Using Honey Pot System” 2016 International Journal for Research & Development in Technology, Volume-5, Issue-5 (May-16); pp.310-314;
ISSN (online):- 2349-3585
[23] Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., & Huang, J. (2020). Rumor detection on social media with bi-directional graph convolutional networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 549–556.
[24] Ibrahim, A. U., Ozsoz, M., Serte, S., Al Turjman, F., & Yakoi, P. S. (2021, January 4).
“Pneumonia classification using deep learning from chest X ray images during COVID- 19”. Springer.
[25] . B. YILMAZ, Y. S. TASPINAR, and M. Koklu, “Classification of Malicious Android Applications Using Naive Bayes and Support Vector Machine Algorithms”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 269–274, May 2022.
[26] Abiyev, R. H., & Ma’aitah, M. K. S. (2018). “Deep convolutional neural networks for chest disease detection.” Hindawi Journal of healthcare and Engineering.
[27] Tilve, A., Nayak, S., Vernekar, S., Turi, D., Shetgonkar, P. R. & Aswale, S. (2020).
“Pneumonia detection using deep learning approaches”. International conference on emerging trends in information technology and engineering.
[28] Hailong, Z., Wenyan, G., & Bo, J. (2014, September). Machine learning and lexicon based methods for sentiment classification: A survey. In 2014 11th web information system and application conference (pp. 262–265). IEEE.
[29] Balahur A, Perea-Ortega JM (2015) Sentiment analysis system adaptation for multilingual processing: the case of tweets. Inf Process Manag 51(4):547–556.
https://doi.org/10.1016/j.ipm.2014.10.004
[30] Appel O, Chiclana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowl Based Syst 108(Supplement C):110–124.
https://doi.org/10.1016/j.knosys.2016.05.040 (new avenues in knowledge bases for natural language processing)
[31] Jain, P. K., & Pamula, R. (2021). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer Science Review, 41. Available at: https://arxiv.org/pdf/2008.10282.pdf
[32] Rathish, C. R., and A. Rajaram. "Efficient path reassessment based on node probability in wireless sensor network." International Journal of Control Theory and Applications 34.2016 (2016): 817-832
[33] S Rahamat Basha, Chhavi Sharma, Farrukh Sayeed, AN Arularasan, PV Pramila, Santaji Krishna Shinde, Bhasker Pant, A Rajaram, Alazar Yeshitla, "Implementation of Reliability Antecedent Forwarding Technique Using Straddling Path Recovery in Manet,"
Wireless Communications & Mobile Computing (Online), vol. 2022, 2022.
[34] Rathish, C. R., and A. Rajaram. "Hierarchical Load Balanced Routing Protocol for Wireless Sensor Networks." International Journal of Applied Engineering Research 10.7 (2015): 16521-16534.
[35] D. N. V. S. L. S. Indira, Rajendra Kumar Ganiya, P. Ashok Babu, A. Jasmine Xavier, L.
Kavisankar, S. Hemalatha, V. Senthilkumar, T. Kavitha, A. Rajaram, Karthik Annam, Alazar Yeshitla, "Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis", BioMed Research International, vol. 2022, 10 pages, 2022.
[36] P. Ganesh, G. B. S. R. Naidu, Korla Swaroopa, R. Rahul, Ahmad Almadhor, C.
Senthilkumar, Durgaprasad Gangodkar, A. Rajaram, Alazar Yeshitla, "Implementation of Hidden Node Detection Scheme for Self-Organization of Data Packet", Wireless Communications and Mobile Computing, vol. 2022, 9 pages, 2022.
https://doi.org/10.1155/2022/1332373.
[37] M. Dinesh, C Arvind, S.S Sreeja Mole, C.S. Subash Kumar, P. Chandra Sekar, K.
Somasundaram, K. Srihari, S. Chandragandhi, Venkatesa Prabhu Sundramurthy, "An Energy Efficient Architecture for Furnace Monitor and Control in Foundry Based on Industry 4.0 Using IoT", Scientific Programming, vol. 2022, Article ID 1128717, 8 pages, 2022. https://doi.org/10.1155/2022/1128717.
