Twitter Sentiment Analysis

By Olga Kolchyna, Thársis Tuani Pinto Souza, Philip C. Treleaven, Tomaso Aste. arXiv.org, 2015

547 citations recorded by the source corpus.

This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learning sentiment classification. To quantify the performance of the main sentiment analysis methods over Twitter we run these algorithms on a benchmark Twitter dataset from the SemEval-2013 competition, task 2-B. The results show that machine learning method based on SVM and Naive Bayes classifiers outperforms the lexicon method. We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach. The combined method proved to produce more precise classifications. We also show that employing a cost-sensitive classifier for highly unbalanced datasets yields an improvement of sentiment classification performance up to 7%.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/ac3f8372b9d893dbdb7e4b9cd3df5ed825ffb548

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Citations in the corpus

Survey on categorical data for neural networks (2020, unknown): “Another important fact about the work Kratzwald et al. report in [47] is that the corpus they apply the GloVe algorithm to, to obtain their pre-trained word embedding is a dataset from a Kaggle Twitter Sentiment Analysis contest in 2017 [83].”
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis (2015, methodology): “Go et. al focused only on classifying tweets and used several approaches to achieve an accuracy of 84.2% with Multinomial Naive Bayes, 79.2% with maximum entropy, and 82.9% using a support vector machine [2].”