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Sentiment Lexicon (SL) is utilized to extract feedback from a large amount of data, which encompass numerous words, in which a few words contain various semantic definitions in various fields, which is called domain-specific (DS) language, and every word under its domain is demonstrated very significantly as their definition varies from one another. Therefore, this research proposes a Self-configuring knowledge graph-based BiLSTM Classifier with Bat–Harris optimization to determine the accurate sentiment from the comment or text message as well as compute whether it is positive, negative, or neutral, by utilizing the feature extraction process which includes Term Frequency-Inverse Document Frequency (TF-IDF), as well as Hybrid word2vec features, and finally detect the sentiment polarity of text. The experimental results which are depending on the performance metrics show that the developed model is improved compared to the prior method, in which the rate of accuracy of the developed scheme on TP 90 is 93.40% and 94.29%, whereas the sensitivity attains the value of 93.31%, and 94.20%, and finally, the specificity acquires the value of 93.68%, and 94.66% based on datasets 1 and 2.

Sentiment lexicon plays an important role in determining the polarity of words and proves to be an important component in sentiment analysis applications. Most of these sentiment lexicons assign a fixed polarity to each word. However, it has been noted that the polarity of words depends on how they are used and so these lexicons are unable to accurately classify the polarity of the sentiments. By considering the aspect and domain of a word will allow us to more accurately classify sentiments. This paper presents a fully automatic method to build an aspect and domain sensitive sentiment lexicon which assigns a polarity to a word depending on both the aspect and the domain. The experimental results show that our lexicon significantly outperforms other commonly used sentiment lexicons / state-of-the-art approaches. To the best of our knowledge, such a lexicon is not publicly available. As such, we also make this lexicon publicly available as we believe it will benefit the research community. In addition, we observe the long tail distribution behavior of product aspects and propose the possibility of aspect ranking by comparing the number of domains and number of sentiment words present for an aspect.

Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded. Sentiment analysis is used to analyze and predict sentiment polarities of the text. This paper provides a survey and gives comparative analyses of the latest articles and techniques pertaining to lexicon-based, traditional machine learning-based, deep learning-based, and hybrid sentiment analysis approaches. These approaches have their own superiority and get the state-of-the-art results on diverse sentiment analysis tasks. Besides, a brief sentiment analysis example in the tourism domain is displayed, illustrating the entire process of sentiment analysis. Furthermore, we create a large table to compare the pros and cons of different types of approaches, and discuss some insights with respect to research trends. In addition, a lot of important sentiment analysis datasets are summarized in this survey.

This paper proposes a method that automatically creates a sentiment lexicon in a new language using a sentiment lexicon in a resource–rich language with only a bilingual dictionary. We resolve some of the difficulties in selecting appropriate senses when translating lexicon, and present a framework that sequentially applies an iterative link analysis algorithm to enhance the quality of lexicons of both the source and target languages. The experimental results have empirically shown to improve the sentiment lexicon in the source language as well as create a good quality lexicon in the new language.