Sentiment analysis for mining texts and social networks data: Methods and tools

Abstract

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

Suggestion Mining from Opinionated Text of Big Social Media Data

“…These suggestions are regarded the phone that was launched by Microsoft. Zucco et al [14] did not define the suggestions in their work; rather, they reported the objectives of the collection of suggestions, which was to progress and improve the quality and functionality of the product, organization, and service. The authors in [25] delivered an algorithm-"GloVE"-to train word embedding to the additional algorithms that highly perform on several benchmark tasks and datasets.…”

“…Moreover, several challenges in suggestion mining approaches relate to analyzing the sentiments of the sentence, identifying the relationship between suggestions, and selecting annotators for supervised and unsupervised learning [14]. Suggestion mining is a recent research area, and thus, studies on extracting suggestions involving different classifiers and algorithms are relatively limited [15].…”

Sentiment Analysis of Financial News: Mechanics and Statistics

“…The full list of CoreNLP wrappers can be found in its website. 8 The survey in [51] introduces 24 utilities for sentiment analysis-9 of these tools have an API for common programming languages. However, several of these utilities are paid, but most of them provide free licenses for a limited period.…”

Finding State of Mind Through Emotion and Sentiment Analysis of the Twitter Text

“…In 1872, Darwin attempted to explain the link between emotions and evolution. Following that, cognitive scientists [6] discovered that emotions are a product of other brain system processes. With development technology, emotional analysis was field of information technology research, also [20] performs the soil corpus transfer learning with better results.…”