scholarly journals Sentiment Classification of Hotel Reviews in Social Media with Decision Tree Learning

2017 ◽  
Vol 158 (5) ◽  
pp. 1-7
Author(s):  
Stanimira Yordanova ◽  
Dorina Kabakchieva
Keyword(s):  
Social Media ◽  
Decision Tree ◽  
Sentiment Classification ◽  
Decision Tree Learning

A gradient boosted decision tree-based sentiment classification of twitter data

2020 ◽  
Vol 18 (04) ◽  
pp. 2050027
Author(s):  
S. Neelakandan ◽  
D. Paulraj
Keyword(s):  
Decision Tree ◽  
Opinion Mining ◽  
Research Topic ◽  
Sentiment Classification ◽  
Decision Tree Classifier ◽  
Twitter Data ◽  
Tree Classifier ◽  
Boosted Decision Tree ◽  
Text Sentiment Analysis

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.

Sentiment Classification of Sinhala Content in Social Media: An Ensemble Approach

2021 ◽  
Author(s):  
Pradeep Jayasuriya ◽  
Ranjiva Munasinghe ◽  
Samantha Thelijjagoda
Keyword(s):  
Social Media ◽  
Sentiment Classification ◽  
Ensemble Approach

Sentiment classification of Sinhala content in social media

2020 ◽  
Author(s):  
Pradeep Jayasuriya ◽  
Sarith Ekanayake ◽  
Ranjiva Munasinghe ◽  
Bihara Kumarasinghe ◽  
Isuru Weerasinghe ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Classification

Sentiment Analysis System for Classification of Patient-Generated Health Reviews Using Rough Set Theory and Machine Learning Technique

2020 ◽  
Vol 10 (10) ◽  
pp. 2361-2368
Author(s):  
Alaa Omran Almagrabi ◽  
Shakeel Ahmad
Keyword(s):  
Social Media ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis System ◽  
Theory Technique

Advancements in social media domain have led to a prominent progress in the number of online communities. Sites, such as Twitter and Facebook, provide an avenue for the unrestricted generation, communication, and distribution of messages as well as information. In this work, we propose a sentiment classification system from patient-generated content posted by users on medical forums and social media sites. The rough set theory is a numerical rule-based technique employed for categorizing and examining doubtful, partial or indistinct data. The emphasis of this study is on the employment of the rough set theory technique for sentiment classification of patient-generated health reviews. We investigated four rough set theory-based algorithms, namely: Genetic, Learning from Examples Module version 2 (LEM2), Exhaustive and Covering, to generate rules for sentiment classification of patient-generated health reviews text. The Rough Set Exploration System (RSES 2.0) software is utilized to conduct experiments. Additionally, we applied SVM classifier to classify emotions. The experimental results show that the Genetic algorithm outperforms the comparing algorithms with an accuracy of 84.2% and Support Vector Machine outperforms other classifiers with an accuracy of 80.5%.

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