Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Abstract

Social media has rapidly expanded over a period of time and generated a huge repository of content. Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement. Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis. This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis. The paper explores and identifies the techniques and tools used in a well-structured approach to find out the research gaps and identify future scope in this area of research. The techniques evolved over time to improve the efficiency of classification. Total 55 research papers are included in this survey. The result reflects that Twitter is the most explored social networking site for opinion mining. Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches. As the latest advancements, Stack based ensemble, fuzzy based and neural network based classifiers are also implemented to enhance the efficiency of classification. WEKA, R Studio, Python are mostly used tools by research scholars for implementation. The overall evolution of the research goes through various changes in terms of technologies, tools, social media platforms and data corpus targeted.

Breaking Barriers in Sentiment Analysis and Text Emotion Detection: Toward a Unified Assessment Framework

“…Rani, Gill, and Gulia summarized 55 research papers related to SA from 2009 to 2021 [54]. They mention that Twitter is the most explored data source for experimentation.…”

Data mining

“…The most important aspect of data mining is selecting a dataset from such a large source [3,7] . Record data, Graph-based records, and Sequential data are the three categories of dataset classifications.…”