Partner Sentiment Analysis for Telkom University on Twitter Social Media Using Decision Tree (CART) Algorithm
Sean Akbar Ryanto*, Donni Richasdy, Widi Astuti
Faculty of Informatics, Informatics Study Program, Telkom University, Bandung, Indonesia
Email: 1,*[email protected], 2donnir@telkomuniversity.ac.id, 3[email protected] Correspondence Author Email: [email protected]
Abstract−Sentiment analysis is an analysis in terms of opinion and meaning in the form of writing. Sentiment analysis is very useful for expressing opinions from any individual or group to improve branding. Branding is a process to promote and improve the name of a brand or brands to attract the attention of consumers to be interested in trying the services of a company that runs in academic terms such as Telkom University. However, this requires cooperation between other associations as partners so that the branding carried out can be effective. One form of cooperation is by providing opinions about Telkom University so that consumers are more familiar with Telkom University on Twitter social media which is the largest social media used by many people because it can provide any opinion freely. Therefore, this study aims to analyze the sentiment submitted by partners for Telkom University on Twitter which is the main factor for promoting themselves to consumers. The process carried out is to take all tweets about Telkom University submitted by partners and then carry out the TF-IDF weighting process and classified using the Decision Tree CART algorithm based on positive, negative, and neutral sentiment categories. The best results obtained by the Decision Tree model of the CART algorithm are the Accuracy value of 86.73%, Precision of 87.06%, Recall of 87.55%, and F1-Score of 86.52%.
Keywords: Sentiment Analysis; Partners; Telkom University; Twitter; Decision Tree (CART).
1. INTRODUCTION
Sentiment analysis is the analysis of the meaning of people's opinions on a descriptor [1]. Sentiment analysis gives the public an understanding to judge something based on opinion [2]. Sentiment analysis of data is very important in expressing meaning to opinions given by an individual or association. It is used to know the person's emotions in a topic of conversation [1]. In the world of work, sentiment analysis is widely used to analyze the opinions of target customers regarding the products or services provided which are used as information for reference.
Information is data that has been processed so that it is useful for people to decide. Information is divided into two categories, such as facts and opinions. Facts are statements about something that has happened and are accompanied by evidence, while opinions or opinions are the way a person expresses himself against something that happened to his situation based on their respective points of view [3]. Therefore, sentiment analysis is very important to analyze the branding of each business in the company.
Branding is a process of defining differences in an organization and then communicating them internally and externally. Within the company, branding helps the company segment the market and helps build stories around products. Branding helps companies with products, marketing, and accounting. Through branding, the company can develop a loyal customer base [4]. In addition to improving reputation and sales levels, branding can build relationships between brands and their customers or between people and their followers [5]. The role of sentiment analysis in branding also serves to improve the brand of each company by analyzing the sentiment of each person to evaluate existing shortcomings and make the company better. This is very important for every company, especially companies engaged in academics such as college campuses.
Telkom University is one of the private universities in Indonesia that is engaged in education to create graduates with integrity and competence with good competitiveness. Telkom University provides 31 study programs under the supervision of seven faculties, one of which is the Informatics study program at the Faculty of Informatics [6]. To improve Telkom University's reputation, cooperation from companies, institutions or other associations as partners is needed [7]. The purpose of the partnership is to provide professional training of specialists based on comprehensive cooperation of the university to attract the attention of customers by combining the results of the analysis, intellectual potential, material, financial and enterprise resources [8]. Therefore, it takes cooperation from partners to be able to do branding at Telkom University better. One example is branding using social media to promote the brand.
Nowadays, people give their opinions through social media. Lately quite a few companies engaged in academies are using social media to find out public opinion. An example is Twitter [9]. Twitter is the most popular social media with millions of users every day in the world [10]. Twitter is a platform where users post, read statuses known as tweets and interact with different users [11]. Twitter is an American social networking service and microblogging company founded in 2006 by Jack Dorsey, Evan Williams, Noah Glass and Biz Stone [12]. The communication platform has 1.3 billion accounts and 336 million active users posting 500 million tweets per day.
Twitter users can upload comments that are limited to 140 characters before October 2018. Currently limited to 280 characters [13].
In this study, the author will conduct a partner sentiment analysis for Telkom University using tweet data that has been collected through twitter and decision tree social media with the CART (Classification And Regression
different machine learning methods such as Random Forest, K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Decision Tree, and Support Vector Machine to conduct sentiment analysis and test sentiment analysis without an approach pre-processing. The results of this study show that the pre-processing approach can affect the results obtained to conduct sentiment analysis on Twitter data, especially in the Decision Tree machine learning method which managed to get the best results compared to other machine learning methods.
Further research has been conducted by Kushankur Ghosh, Arghasree Banerjee, Sankhadeep Chatterjee, Soumya Sen with the title "Imbalanced Twitter Sentiment Analysis using Minority Oversampling" in 2019 [17].
The study aims to overcome the problem of imbalance data in sentiment analysis by conducting a trial of minority oversamplin methods to balance data. Researchers approached using the SMOTE (Synthetic Minority Oversampling Technique) algorithm. The results of this study prove that minority oversampling can overcome the problem of unbalanced data to conduct sentiment analysis and have a good impact on improving the sentiment analysis results obtained.
Another research conducted by S.Kasthuri and Dr.A.Nisha Jebaseeli with the title "An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis" in 2020 [18]. This study aims to test how effective the Decision Tree machine learning method is to be used in sentiment analysis. This study has also tested a topic modelling approach using LDA (Latent Dirichlet Allocation) using a sentiment analysis program that has been created to extract keywords and help identify the topic in question. The results of this study show that the proposal to increase the accuracy of sentiment analysis by up to 6-20% is related to the existing system.
There is also a study conducted by Muhammad Hammad, and Haris Anwar with the title "Sentiment Analysis of Sindhi Tweets Dataset using Supervised Machine Learning Techniques" in 2019 [19]. The purpose of this study is to examine Sindhi language because the research that exists so far is only research on reasonable languages such as English, German and so on. However, for Sindhi, which is quite rarely found, it is still lacking in terms of research. The data used for the study came from tweets with Sindhi language on social media Twitter. The results showed that Decision Tree and K-Nearest Neighbors provided the best accuracy on Sindhi's tweet dataset followed by SVM (Support Vector Machine).
In a study conducted by Achmad Bayhaqy, Kaman Nainggolan, Sfenrianto Sfenrianto, and Emil R. Kaburuan with the title "Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes" in 2018 [20]. This study aims to compare the best classification methods between Decision Tree, K- Nearest Neighbor and Naïve Bayes on sentiment analysis towards E-Commerce. The data source used in the study used tweets about Tokopedia and Bukalapak E-Commerce on Twitter social media. The results of the research that has been carried out by researchers obtained Accuracy from the Decision Tree, K-Nearest Neighbors, and Naïve Bayes methods by 80%, 78%, and 77%, respectively. Precision results from the Decision Tree, K- Nearest Neighbor, and Naive Bayes methods were 79.96%, 85.67%, and 88.50%, respectively. Meanwhile, recall results from Decision Tree, K-Nearest Neighbor, and Naïve Bayes were 84%, 70%, and 64%, respectively.
2. RESEARCH METHODOLOGY
2.1 System Design Flow
There are several stages to create a partner sentiment analysis system for Telkom University. The following is the flow of the stages that will be carried out.
a. The first stage is to collect tweet data from Telkom University partner accounts to be used as datasets in sentiment analysis models using the crawling method,
b. Labelling the tweet data obtained based on 3 label categories, namely positive, neutral, and negative, c. Perform several stages of pre-processing the dataset such as data cleaning, case folding, tokenization,
normalization, stopwords removal, and stemming,
d. Perform data splitting to be used as a data train and data test with data ratios of 80:20, 75:25, and 70:30, e. Weighting every word in the dataset with TF-IDF feature extraction,
f. Conduct training on models based on data trains with the Decision Tree method of the CART algorithm, g. The last stage is to evaluate the performance of the model that has been made based on the calculation of the
confusion matrix.
Figure 1 is a flowchart visualization of making a sentiment analysis system.
Figure 1. Sentiment Analysis Flowchart 2.2 Dataset
The dataset used in this study used 1855 tweet data from Telkom University internal partner accounts such as
@TelUCareer, @smbtelkom, @StudentsTelU, and @InfoUnivTelkom on Twitter social media obtained by crawling using the twint library in the python programming language from December 31, 2017, to March 22, 2022.
After that, labeling of the dataset was carried out with 3 label categories such as positive, neutral, and negative given by 3 different labelers. The purpose of this data labeling process is to train data to be used as a classification model, so that the model can be used to make classification predictions. Table 1 is some examples of the data obtained.
Table 1. Research Datasets
No. Tweet Label
1 Hai hai #telutizen Ayo daftarkan dirimu sekarang melalui Jalur UTBK #telkomuniversity
cek persyaratan di bawah ini dan segera kunjungi web kami di https://t.co/Sstvm31AR4
semangat!!! #smbtelkom https://t.co/sBWfQ3Qmvm
Positive
2 Kali ini, thread berasal dari cerita Ni Nafisa Rihadatul Aisy S1 Ilmu Komunikasi 2019 (@cacatrashy) dengan tema Event kampus di Telyu! Ada apa aja sih! kita mulai ya
Neutral 3 @ajiwijaya76 Tidak bisa dik huhuhu cek pinned tweet kami ya untuk penjelasannya Negative
Figure 2. Number of Labels on the Dataset
Based on the visualization for the number of each data label in Figure 2, it is evident that the number of each label is unbalanced. For neutral labels, they have the highest amount of data with a total of 1156 data, then positive labels with a total of 457 data and negative labels with the least number of 239 data.
2.3 Pre-processing Data
Pre-processing is the initial stage in classifying data. Data pre-processing is quite important in effective text mining to improve data quality and improve classification accuracy [21]. The purpose of pre-processing is so that the data can be easier to process and then produce quality data for the classification process. There are several processes that must be done to pre-process the data to be used. The following is the pre-processing process carried out.
a. Cleaning data serves to clean data to leave text data only while eliminating punctuation marks, numbers, symbols, emoticons or emojis, and links or URLs,
Table 2 is an example of the results after doing the pre-processing stage.
Table 2. Pre-processing
Pre-Processing Pre-Processing Results
Initial Data Kali ini, thread berasal dari cerita Ni Nafisa Rihadatul Aisy S1 Ilmu Komunikasi 2019 (@cacatrashy) dengan tema Event kampus di Telyu! Ada apa aja sih! kita mulai ya Data Cleaning Kali ini thread berasal dari cerita Ni Nafisa Rihadatul Aisy S Ilmu Komunikasi
cacatrashy dengan tema Event kampus di Telyu Ada apa aja sih kita mulai ya
Case Folding kali ini thread berasal dari cerita ni nafisa rihadatul aisy s ilmu komunikasi cacatrashy dengan tema event kampus di telyu ada apa aja sih kita mulai ya
Tokenization ‘kali’, ‘ini’, ‘thread’, ‘berasal’, ‘dari’, ‘cerita’, ‘ni’, ‘nafisa’, ‘rihadatul’, ‘aisy’, ‘s’,
‘ilmu’, ‘komunikasi’, ‘cacatrashy’, ‘dengan’, ‘tema’, ‘event’, ‘kampus’, ‘di’, ‘telyu’,
‘ada’, ‘apa’, ‘aja’, ‘sih’, ‘kita’, ‘mulai’, ‘ya’
Normalization ‘kali’, ‘ini’, ‘thread’, ‘berasal’, ‘dari’, ‘cerita’, ‘ni’, ‘nafisa’, ‘rihadatul’, ‘aisy’, ‘s’,
‘ilmu’, ‘komunikasi’, ‘cacatrashy’, ‘dengan’, ‘tema’, ‘event’, ‘kampus’, ‘di’, ‘telyu’,
‘ada’, ‘apa’, ‘saja’, ‘sih’, ‘kita’, ‘mulai’, ‘iya’
Stopwords Removal
‘thread’, ‘berasal’, ‘dari’, ‘cerita’, ‘ni’, ‘nafisa’, ‘rihadatul’, ‘aisy’, ‘s’, ‘ilmu’,
‘komunikasi’, ‘cacatrashy’, ‘tema’, ‘event’, ‘kampus’, ‘telyu’, ‘ada’, ‘apa’, ‘kita’,
‘mulai’, ‘iya’
Stemming ‘thread’, ‘berasal’, ‘dari’, ‘cerita’, ‘ni’, ‘nafisa’, ‘rihadatul’, ‘aisy’, ‘s’, ‘ilmu’,
‘komunikasi’, ‘cacatrashy’, ‘tema’, ‘event’, ‘kampus’, ‘di’, ‘telyu’, ‘ada’, ‘apa’, ‘kita’,
‘mulai’, ‘iya’
2.4 Term Frequency-Inverse Document Frequency (TF-IDF)
Feature extraction is an important stage in sentiment analysis [22]. Term Frequency–Inverse Document Frequency (TF-IDF) is a way to extract features [23]. TF-IDF is a statistical method that combines the TF (Term Frequency) Technique and the IDF (Inverse Document Frequency) technique that serves as a statistic to reveal how relevant a word is to a document in a document set [24]. To calculate the TF-IDF value is to multiply the Term Frequency value by the Inverse Document Frequency value [25]. Equation (1) is the IDF calculation formula.
𝑖𝑑𝑓𝑗= 𝑙𝑜𝑔 (𝐷
𝑑𝑓𝑗) (1)
After that, the TF-IDF calculation can be carried out to get the result. Equation (2) is the calculation formula of the TF-IDF.
𝑤𝑖𝑗= 𝑡𝑓𝑖𝑗 × 𝑖𝑑𝑓𝑗 (2)
The following is a description of the formula that has been described.
𝑡𝑓𝑖𝑗 : Number of term occurrences in a document 𝑤𝑖𝑗 : Term weights on documents
𝐷 : Sum of all documents
𝑖𝑑𝑓𝑗 : Term distribution on documents 𝑑𝑓𝑗 : Number of documents containing terms 2.5 Decision Tree (CART)
Decision Tree is a popular classification method because it is easy to interpret. Decision Tree is a prediction model using decision tree structures or hierarchical structures. In principle, decision trees are used to predict a person into the form of a different category or class, considering the value corresponding to its attribute. The flexibility of this technique provides suggestive visualization [26]. In the Decision Tree there is the top node called the root node, the internal node is a feature of the data, and the leaf node represents the result. Figure 3 is an illustration of a Decision Tree.
Figure 3. Decision Tree
Classification And Regression Tree (CART) is one of the Decision Tree algorithms in machine learning that uses values from predictor variables used to divide datasets into groups depending on the data of each category [27]. The CART algorithm is based on a recursive algorithm, so each node has two branches called binary trees [28]. CART uses the Gini index formula to split to select features and determine the optimal binary segmentation point [29]. Equation (3) is the calculation formula of the Gini Index.
𝐺𝐼𝑁𝐼(𝑡) = 1 − ∑ [𝑝(𝑗|𝑡)]𝑗 2 (3)
Gini index serves to calculate purity. The following is a description of the formula described.
p (j | t) : Relative frequency of class j on node t 2.6 Evaluation
Confusion matrix is widely used in machine learning to evaluate supervised classification results [30]. Confusion matrix consists of 4 important components such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN). In addition, there is performance evaluation with calculations such as Accuracy, Precision, Recall, and F1-Score. The following is the calculation formula for the performance evaluation.
a. Accuracy is a calculation of how accurately a model can classify data correctly. Accuracy is the degree of proximity of the predicted value to the actual value. Equation (4) is an Accuracy calculation formula.
The unit of accuracy calculation uses percent (%).
𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 +𝑇𝑁
𝑇𝑃 +𝐹𝑃 +𝑇𝑁 +𝐹𝑁 (4)
b. Precision is a calculation for the degree of accuracy between the requested data and the predicted results given by the model. Equation (5) is the calculation formula of Precision. The unit of the calculation of Precision uses percent (%).
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃
𝑇𝑃 +𝐹𝑃 (5)
c. Recall is a calculation of a model's success in rediscovering information. Equation (6) is the Recall calculation formula.
𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃
𝑇𝑃 +𝐹𝑁 (6)
d. F1-Score is a performance matrix considering Recall and Precision calculations. Equation (7) is the F1-Score calculation formula.
𝐹1 𝑆𝑐𝑜𝑟𝑒 = 2 × 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ×𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 +𝑟𝑒𝑐𝑎𝑙𝑙 (7)
3. RESULT AND DISCUSSION
In this study, there are 3 test scenarios to evaluate the system that has been built. Scenario 1 is a test to compare data that was before oversampling and after oversampling because the dataset that has been obtained is not balanced, therefore this test scenario aims to see the results of a comparison of the sentiment analysis system with datasets that have not been balanced and those that have been balanced. Scenario 2 aims to find the best pre- processing combination for the system that has been created based on the dataset owned. Then, scenario 3 aims to compare the effect of TF-IDF feature extraction with unigrams and bigrams on sentiment analysis.
3.1 Scenario 1 The Effect of Oversampling on Unbalanced Datasets
This test scenario aims to find out how much influence Telkom University partner sentiment datasets have on the Decision Tree model the CART algorithm takes a minority oversampling approach to balance the number of labels
Oversampling
70:30 70.91 68.70 53.65 52.28 51.00
With Oversampling
80:20 76.27 76.22 76.51 82.08 76.35
75:25 77.04 77.04 77.28 82.46 77.41
70:30 76.51 76.75 76.86 82.24 76.97
To make the test results look clearer, Figure 4 is a comparison visualization before and after the minority oversampling approach is based on a data ratio of 75% train data and 25% test data showing the best results from each comparison.
Figure 4. Visualization of Results Before and After Oversampling
Based on the results obtained from this 1st test scenario, the sentiment analysis model created using the Decision Tree CART algorithm has much better performance results when taken a minority oversampling approach. The results obtained from this test scenario are an Accuracy value of 77.04%, a Precision value of 77.28%, a Recall value of 82.46%, and an F1-Score value of 77.41%. Dataset that has been balanced with minority oversampling can get better results because the model can be trained using a more varied data train, so that the model can better predict new data by concluding based on the model training carried out.
3.2 Scenario 2 The Effect of Pre-processing on Datasets
From the results of the previous test scenario, the dataset that has been carried out by the minority oversampling approach will be used as the dataset used for the next test scenario. For this test scenario 2, it will conduct trials of several stages of pre-processing. The test that will be carried out is to compare the results of the data that has been pre-processing completely (data cleaning, case folding, tokenization, normalization, stopwords removal, stemming) with the results of the data carried out in the pre-processing stage without stopwords removal and stemming. In addition, it will also be compared with the results of the data without any pre-processing at all. Table 4 is the result of the comparison of the pre-processing stage of the trial to the dataset.
Table 4. Pre-Processing Comparison Pre-Processing Data
Ratio
Accuracy
(Train) Accuracy Precision Recall F1-Score Complete Pre-
Processing
80:20 76.27 76.22 76.51 82.08 76.35
75:25 77.04 77.04 77.28 82.46 77.41
70:30 76.51 76.75 76.86 82.24 76.97
Without Stopwords Removal &
Stemming
80:20 85.40 86.31 86.59 86.76 86.10
75:25 83.92 86.73 87.06 87.55 86.52
70:30 83.88 85.30 85.58 86.00 85.08
0 10 20 30 40 50 60 70 80 90
No Oversampling Oversampling
Accuracy(Train) Accuracy Precision Recall F1-Score
Pre-Processing Data Ratio
Accuracy
(Train) Accuracy Precision Recall F1-Score Without Pre-
Processing
80:20 82.37 81.55 81.91 82.97 81.38
75:25 82.27 81.89 82.24 82.25 81.92
70:30 82.73 80.49 80.81 81.44 80.41
For clearer results, Figure 5 is a visualization of the results of the comparison of the pre-processing effect on the dataset in the Decision Tree model of the CART algorithm using a data ratio of 75% train data and 25%
test data because the data ratio displays the best results from each comparison.
Figure 5. Visualization of Comparative Results of The Effect of Pre-Processing
From the results of this 2nd test scenario, it is proven that carrying out the complete pre-processing stage of the book produces the best performance. Therefore, the pre-processing stage must be further adjusted to the dataset owned to achieve the best results from the Decision Tree model CART algorithm that has been created. The results obtained from this test scenario are an Accuracy value of 86.73%, a Precision value of 87.06%, a Recall value of 87.55%, and an F1-Score value of 86.52%. The model that dataset is carried out pre-processing techniques without stopwords removal and stemming get more accurate results because the stopwords removal technique is a technique to eliminate words that are considered unimportant so that there is a high probability of errors in the model because it has eliminated words that should be important information that should not be omitted for example such as the sentence 'alumni dan endowment' if the word 'dan' is omitted then the result will be 'alumni endowment' so that the meaning of the sentence becomes different and makes the model make mistakes when classifying. For stemming techniques do not need to be used because the technique serves to convert a word into its basic word, thus causing the meaning of each word to be different and causing the model to misclassify the example such as the word 'lakukan' being changed to 'laku' resulting in differences in the meaning of the word.
3.3 Scenario 3 The Influence of Unigram and Bigram on Sentiment Analysis
Based on the results of test scenario 2, datasets that have been pre-processed without stopwords removal and stemming will be used as datasets used for test scenario 3. In test scenario 3 will conduct a trial to find out how much influence the addition of N-Gram has on the extraction of TF-IDF features, especially for unigrams and bigrams on sentiment analysis models using the Decision Tree method of the CART algorithm. Table 5 is the result of a comparison of the use of unigram and bigram in the model.
Table 5. Comparison of Unigram and Bigram N-Gram Data Ratio Accuracy
(Train) Accuracy Precision Recall F1-Score Unigram
80:20 85.40 86.31 86.59 86.76 86.10
75:25 83.92 86.73 87.06 87.55 86.52
70:30 83.88 85.30 85.58 86.00 85.08
Bigram
80:20 70.87 71.18 71.53 81.09 70.86
75:25 70.32 71.74 72.06 82.00 71.42
70:30 71.28 70.50 70.61 80.77 69.96
To see the comparison, Figure 6 is a visualization of the results of the comparison of unigrams and bigrams on the sentiment analysis model of the Decision Tree model of the CART algorithm using a data ratio of 75% train data and 25% test data because the data ratio displays the best results from each comparison.
70 72 74 76 78 80 82 84 86 88 90
Full Pre-processing W/O Stopword &
Stemming
W/O Pre-processing
Accuracy (Train) Accuracy Precision Recall F1-Score
Figure 6. Visualization of Unigram and Bigram Comparison Results
The results of this 3rd test scenario show that the extraction of unigram TF-IDF features has a better influence on the Decision Tree model of the CART algorithm than the extraction of bigram TF-IDF features. The results obtained from the use of unigrams in the sentiment analysis model are with an Accuracy value of 86.73%, a Precision value of 87.06%, a Recall value of 87.55%, and an F1-Score value of 86.52%. Extraction of the TF- IDF feature with unigrams gets better results than bigram because the use of unigrams which is the breakdown of a sentence into words produces more information found in the data train for model training than bigram which is the splicing of a sentence into per 2 words.
4. CONCLUSION
After conducting sentiment analysis research on Telkom University partners on Twitter social media using the Decision Tree CART algorithm with a total dataset of 1855 data given 3 labels, namely positive, neutral and negative, it was found that the creation of a classification model with a data train ratio of 75% and a test data of 25% produced a good model because the appropriate data sharing saw the amount of data that was not too much many. In addition, the use of minority oversampling can affect the classification model so that the model can conduct training with varied data trains so that the model can predict optimally. The use of pre-processing techniques without using stopwords removal and stemming also affects the sentiment analysis model so that the data train used for model training does not lack information to make predictions. The use of unigrams in the extraction of TF-IDF features for models also results in the best performance for the model because it can further expand the information obtained to train the classification model to predict better. The best results obtained by the Decision Tree model of the CART algorithm are with an Accuracy value of 86.73%, a Precision value of 87.06%, a Recall value of 87.55%, and an F1-Score value of 86.52%. The suggestion for further research is to try using datasets with an even greater amount of data to find out whether the amount of data owned can affect models that use the Decision Tree CART algorithm. In addition, you can test various Decision Tree parameters to find out how much they affect the sentiment analysis model.
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