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Sentiment analysis for mining texts and social networks data: Methods and tools
Author(s) -
Zucco Chiara,
Calabrese Barbara,
Agapito Giuseppe,
Guzzi Pietro H.,
Cannataro Mario
Publication year - 2019
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1333
Subject(s) - sentiment analysis , computer science , flexibility (engineering) , usability , data science , world wide web , information retrieval , artificial intelligence , human–computer interaction , statistics , mathematics
Social networks (SNs) represent an established environment in which users share daily emotions and opinions. Therefore, they have become an essential source of big data related to sentiment/opinion sphere. Sentiment analysis (SA) aims to extract sentiments, emotions or opinions from texts, made available by different data sources like SNs. This review presents a depth study relative to the methods and the main tools for SA. The analysis was performed by defining four criteria and several variables to compare 24 tools with objective criteria. Specifically, the tools have been analyzed and tested to verify their usability, flexibility of use, and other specifications related to the type of analysis performed. The majority of tools can detect positive, negative, and neutral polarity, while few tools only detect positive and negative polarity. Moreover, seven tools were able to recognize emotions, and only one provides a visual map for geo‐referenced data. Except for one, remaining 23 tools offer service through the web interface. Finally, only nine tools provide both application program interfaces and a client for common programming languages to allow potential developer end‐users to integrate a specific SA tool into their application. Differently, from other recent surveys, the paper presents and discusses both methods and tools for analyzing texts and SN data sources to extract sentiment. Moreover, it contains a comprehensive comparison with other recent surveys. The comparative analysis of the tools completed according to objective criteria allows to highlight some limits on main tools that need to be faced with enhancing the end‐user experience. This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Data Mining Software Tools