The widespread use of the social media network for communication, social participation, and self-expression has resulted in many individual data being willingly disclosed online. The social disclosure on social media provides a platform for the researchers to measure general opinion unremarkably [1]. Computer technology-based social media platforms have benefited people for self-expression, communication, and social participation. These social media sources are responsible for sharing text, audio, and video links between individuals worldwide to be well informed and keeping in touch with others. This explosive social development can be seen on Facebook, Linked In, Friend Feed, Myspace, and many more, but the flow of Twitter is much more than other platforms [2]. Many people have been attracted by social media platforms such as Instagram, Twitter, and Facebook, which allow them to express their thoughts, feelings, and ideas about various people, places, and things. People give their views on these social websites for the political party election campaign. This study focuses primarily on Pakistan for the general election held in 2018. We found that the predictive power of social media performs well for Punjab province due to the population and people's tendency towards social media. However, the social media platform was less useful for Baluchistan.
Nowadays, using social media platforms to stream opinions, emotions, or beliefs about personalities, places, or things exponentially increases. Sentiment analysis methods are mainly classified as Lexicon-based [3], machine-learning-based [4], and hybrid. In addition, other categorizations are presented in [5] with hybrid approaches, knowledge-based and statistical categories. There is increasing research in broad areas to evaluate computational sentiments and opinions. Therefore, the data available on social media is used to mining facts that are further used to predict elections. Social media behavioral analysis can achieve sentiment analysis accuracy and forecasting [6]. Sentiment analysis is the artificial intelligence process in which people share their emotional beliefs or opinions about different things such as events, places, political parties, and personalities. The trend of using this platform for sharing views is becoming more popular in elections. Before the election, party workers start a campaign for a political party of their interest. Recently in the 2018 election in Pakistan, several political parties began a trend on social media, such as “Naya Pakistan,” “Vote Ko Izzat Do” and “Rotti Kapra Makkan.” These were the top trend on social media, and people shared different emotions and opinions accordingly. Many believed that the election results were fair, and few thought that the results were unfair. These emotions and beliefs are classified as positive, neutral, or negative. To extract the opinion of individuals regarding the trends on social media is challenging. In the 2009 German federal election, more than 100,000 messages were processed using sentiment analysis referred to either a political candidate or a political party [7]. In recent years, we have seen a dramatic increase in the use of social networks.
The popularity of Pakistan's political parties in the general election 2013 was tested using a keyword-based tweet collection that concentrated on political parties' names and political celebrities [8]. The dataset was tested on both unsupervised and supervised algorithms such as K-nearest neighbors (KNN) [9], naïve Bayes (NB) [10], random forest, support vector machines (SVM) [11], Naïve Bayes multinomial (NBMN) [12] applied prind and rainbow tool classification technique on unigram data. Porter and Laplace stemmer [12, 13] remove zeros from the data to smooth the data. Inverse document frequency and Term frequency (TF–IDF) [14] were used for relevant documents to find words that are related strongly. It also used the Waikato for Analysis of Knowledge environment to perform 5-fold cross-validation in (Weka) [15, 16]. The initial intention of choosing data from Twitter is to gain information better from this stage since Twitter holds the politicians authenticated accounts, not Instagram or Facebook. Similarly, contrary to Facebook, Twitter confines users to give their complete and compact opinions in 280 characters.
In [17, 18], it has been confirmed that, with Twitter, people can gain knowledge from their accounts instead of gathering information on traditional methods of observation. In addition, the authors in [19] developed a model for harnessing the emotions from tweets, while large-scale data were analyzed [20, 21] for sentiment analysis. Social communities have been described with an influential impact in [22], which has a metrical meaning attributed to each emotional user's position. As a result, the paper's input involves election sentiment analyses obtained from users on Twitter, with separate sentiment analyzers. Moreover, using machine learning classifiers, we receive the validation results from each analyzer [23] our experiment basis on comparing several sentiment analyzers and validating the results of the different classifiers.
The remaining of the paper is organized as follows. Section 2 covers related literature in the domain of sentiment analysis on Twitter-based election prediction results. Section 3 presents the proposed technique, while Sect. 4 includes the experimental discussion and results. Finally, Sect. 5 concludes the paper along with future directions.