Despite the growing emergence of new computer analytic software programs, the adoption and application of computer-based data mining and processing methods remain sparse in literary studies and analyses. This study proposes a text analytics lifecycle to detect and visualize the prevailing themes in a corpus of literary texts. Two objectives are to be pursued: First, the study seeks to apply a Topic Modeling approach with selected algorithms of LDA, LSI, NMF, and HDP that can effectively detect the recurring topics about the major themes developed in the dataset. Second, the project aims to apply a Sentiment Analysis model that can analyze the polarity of writers’ discourse on the detected thematic topics with the algorithms of Vader and TextBlob. The implementation of Topic Modeling has detected six thematic topics of sex, family, revolution, imprisonment, intellectual, and death. The adoption of the Sentiment Analysis model also revealed that the feelings attached to all the identified themes are largely negative sentiments expressed towards socio-political issues.

In recent years, South-Asian literature in English has experienced a surge of newfound love and popularity both in the local and the global market. In this regard, Arundhati Roy's The God of Small Things (1997) has garnered an astounding mix of positive and negative reactions from readers across the globe. This chapter adopts an artificial intelligence approach to analyse netizen readers' feedback on the novel as documented in the book cataloguing website Goodreads. To this end, an opinion mining framework is proposed based on artificial intelligence techniques such as topic modelling and sentiment analysis. Latent semantic analysis (LSA) and latent Dirichlet allocation (LDA) are applied and compared to find the abstract “topics” that occur in a collection of reviews. Furthermore, lexicon-based sentiment analysis approaches such as Vader and Textblob algorithms are used and compared to find the review sentiment polarities.

The advent of Coronavirus-19 (COVID-19) has created a new threat in terms of economy and life. Frankly, the adverse effects of COVID-19 can put our life at risk if we are contaminated. The recent promising cases in Malaysia have certainly intensified from day to day, going from bad to worse. The primary factor that causes the raising problem of COVID cases is the absence of cooperation between Malaysian and government. This research aims at visualizing the current situation of COVID-19 and thus raising consciousness among Malaysians to solve this dilemma. To fulfill the objectives, there are two stages of processes need to be performed. Using the dataset from Internet, the first section would use Microsoft Excel to create visualization tools such as a pie chart and a line chart. Next, the second part will scrap the Twitter data to explore how Malaysians are aware of COVID by using “Twint” function in Python software. The finding reveals that current COVID situation in Malaysia is in a severe stage since the chart shows that it has an exponential growth.  Moreover, the Twitter activity has indicated that the people are not paying attention to the COVID topic shared by Malaysia Ministry of Healthy (MOH) Consequently, the new positive cases increase dramatically after September 2020 in Malaysia. In conclusion, the people are more concern to the COVID news from MOH during the implementation of MCO and CMCO. The people lose concern when the number of cases dropped or the MCO and CMCO is ended.