Abstract
Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).
Highlights
In recent years, a huge number of people have been attracted to social-networking platforms like Facebook, Twitter and Instagram
After the collection of data, the study performed automatic buzzer detection to remove unnecessary tweets and analyzed the tweets sentimentally by breaking each tweet into several sub-tweets. It calculated sentiment polarity and, to predict election outcome, used positive tweets associated with each candidate, and used mean absolute error (MAE) [33] in order to measure the performance of the prediction and make the claim that this Twitter-based prediction was 0.61% better than the same type of surveys conducted traditionally
At the first step of sentiment analysis with the aim of determining the accuracy of these analyzers, tweets were analyzed from various sentiment analyzers
Summary
A huge number of people have been attracted to social-networking platforms like Facebook, Twitter and Instagram. Methods of sentiment analysis can be categorized predominantly [1] as machine-learning [2], Lexicon-based [3] and hybrid [4,5]. A keyword-based tweet collection, focused on the names of the political parties and political celebrities of Pakistan [11], was made to test the popularity of the party for the elections of 2013 This dataset was tested with both supervised and unsupervised machine-learning algorithms. This paper presents the validation of results obtained from each analyzer with machine-learning classifiers.
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