Comparing and combining sentiment analysis methods

By Pollyanna Gonçalves, Matheus Araújo, Fabrício Benevenuto, Meeyoung Cha. Conference on Online Social Networks, 2013

447 citations recorded by the source corpus.

Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a316fc6ffa01fd9027a7f69fe19b2e81534b4264

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Citations in the corpus

Sentiment Analysis in Tourism: Capitalizing on Big Data (2019, methodology): “Different sentiment analysis methods were developed in various domains, triggering a small number of review papers on this topic (Gonçalves et al., 2013; O’Leary, 2011; Ribeiro et al., 2016).”
Detecting Emotional Contagion in Massive Social Networks (2014, methodology): “Although this is not the only way to measure sentiment [27], this method has previously been used to measure the emotional content of online messages [28].”
Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis (2021, methodology): “Six supervised ML algorithms are used; they are Logistic Regression (LR) [45], Ridge Classiﬁer (RC) [46], Support Vector Machine (SVC) [47], Decision Tree (DT) [48], Random Forest (RF), and Naive Bayes (NB) classiﬁers.”