The Process of Sentiment Analysis: A Study

Abstract

The paper gives a detailed report of the concept-sentiment analysis and its usage. It explains the various real world applications of sentiment analysis along with the workflow that describes the execution of this analysis. The recent techniques used in the analysis have been described briefly and the appropriate performance metrics have been applied to them. The paper enlightens the need of sentiment analysis and its importance.

Social media big data and capital markets—An overview

“…An advantage of the machine-learning method lies in its ability to be defined in detail for specific contexts. On the contrary, a disadvantage is its low applicability to new data due to costly text tagging or unavailability of sufficient examples from which the machine-learning program can learn (Annett and Kondrak, 2008;Gonçalves et al, 2013;Godsay, 2015). 2.…”

“…Data transformation from a qualitative into a quantitative nature based on the machine-learning or lexical method. Further technical details of sentiment analysis process are provided in the research of Pang and Lee (2008), Russel (2011), Liu (2012) or Godsay (2015).…”

“…Big data processing, in another words sentiment analysis, can be considered as one of the most important points in big data research. Sentiment analysis or opinion mining describes various computational techniques focused to discover, extract and distil the human emotions, feelings or opinions from textual information within the web content towards the certain entities (Fang and Zhan, 2015;Godsay, 2015). Today, sentiment analysis has evolved into an autonomous and currently popular field of study.…”

Sentiment Analysis- A tool for Data Mining in Big Data Analytics

“…(i.e., positive or negative). This methodology requires labelled information to prepare the classifiers [13]. There are 3 basic algorithms followed by the machine learning method: Naive Bayes classification, maximum entropy classification, and support vector machines [14].…”

“…In contrast to this, we have the lexical-based techniques that utilizes a predefined rundown of words, where each word is related with an explicit emotion. The lexical techniques fluctuate as per the data set for which they were made [13]. It also involves understanding the connection between the sentiment expressed and the document in question by calculating the semantics of the words in the data set [15].…”