# Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
## 2 Literature Review
### Lexicon-Based Sentiment Analysis: A Review  
One of the best examples of a lexicon is the shouts exchanged among the players in a match such as \"Yeah!\", \"Hool\", \"Hutt\", \"Blitz!\", \"Hike!\", etc. Another set of examples is words used by lawyers in court: \"I object, my lord\", \"court adjourns\", \"counsel\", etc. These sets of terminologies that are used among a group of people are known as lexicons. The phrases that have specific meanings are called lexemes. Different languages have different words with the same meaning; for example, water is thanni (Tamil), vellam (Malayalam), paani (Hindi), etc. Lexicon-based SAs have two approaches--a dictionary-based approach and a corpus-based approach. The dictionary-based method contains words with semantic orientation, and the corpus-based approach has words with and without sentiments that can be used for other purposes as well [39]. Figure 5 shows the architecture of the lexicon-based SA, and it shows how the view is classified and the opinion is extracted from the new data. The data are trained by the learning model that classifies the data into three forms--positive, negative, and neutral. Lexicon-based SA is used when the training data are inadequate. According to [PERSON] et al. [40], unigrams were used in previous algorithms, but they did not provide satisfactory results. Hence, the authors proposed the n-grams method which is formed by the \(N\) number of unigrams to offer better results. For negative statements in the document, the authors proposed to use a ratio-based approach. Another challenge to the domain are heterogeneity and linguistic problems. The domain that is specific to the sentiment can differ from content to content; however, when it comes to language, it differs from person to person [41]. To overcome these kinds of challenges, an algorithm that is domain-specific should be taken up with already existing lexicon-based domain-specific methods and with the utilization of undefined dictionary-based sentiment analysis (DBSA) and corpus-based sentiment analysis lexicons (CBSALs). \begin{table}
\begin{tabular}{p{12.3 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  & Sentiment & & & Extractive \\  & compression & & & compression \\  & technique before the aspect-based sentiment analysis & Chinese blog review dataset & 88.78\% & & technique failed to \\  & & & & achieve & \\ \hline  & & & & accuracy & \\ \hline  & & & & & \\ \multirow{-2}{*}{[38]} & For enhancing the prediction accuracy of the unemployment rate & & Unemployment Initial Claims (UICs) values between January 2004 and March 2012, US Department of Labor & & Rate prediction was not accurate \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._A few more past lexicon-based works are presented in Table 3 below. A few more past lexicon-based works are presented in Table 3 below. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Level of Analysis** & **Approach** & **Dataset Used** & **Results** \\ \hline
[42] & Feature-based term weight & Term Frequency (TF) & Multi-domain dataset & 85\% \\ \hline
[43] & - & Support Vector Machine & Tweets & 75.5\% \\ \hline
[44] & Feature based &
\begin{tabular}{c} Polarity classifier, \\ enhanced emoticon classifier \\ SentiWordNet-based classifier \\ \end{tabular} & Tweets & 87\% \\ \hline
[45] & - & Relative frequency word count & Drug, car, hotel & 80\% \\ \hline
[46] & Word level &
\begin{tabular}{c} Relative frequency word count \\ (RFWC) \\ \end{tabular} & Tweets & Less than 50\% \\ \hline
[47] & Sentence level & Weight scheme & Tweets & 70\% \\ \hline
[48] & Phrase level & OpenDover (web service) & Tweets & 75\% \\ \hline
[49] & Sentence level &
\begin{tabular}{c} Semantic-based lexicon \\ analysis \\ \end{tabular} & Tweets & - \\ \hline
[50] & Word level & Word emotion lexicon & \
\begin{tabular}{c} Socio-political (SP) and \\ sports \\ \end{tabular} &
\begin{tabular}{c} Did not add \\ value \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}