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Of late, text and sentiment analysis have become essential parts of modern marketing. These play a vital role in the division of natural language processing (NLP). It mainly focuses on text classification to examine the intention of the processed text; it can be of positive or negative types. Sentiment analysis dealt with the computational treatment of sentiments, opinions, and subjectivity of text. This chapter tackles a comprehensive approach for the past research solutions that includes various algorithms, enhancements, and applications. This chapter primarily focuses on three aspects. Firstly, the authors present a systematic review of recent works done in the area of text and sentiment analysis; second, they emphasize major concepts, components, functionalities, and classification techniques of text and sentiment analysis. Finally, they provide a comparative study of text and sentiment analysis on the basis of trending research approaches. They conclude the chapter with future directions.

Recommender systems inherently dynamic in nature and exponentially grow with time, in terms of interests and behaviour patterns. Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is constant. This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic community algorithm in order to exploit the temporal changes in the community structure to enhance the existing system. Our framework also provides solutions to common inherent issues of collaborative filtering approach such as cold-start, sparsity and compared against static and traditional collaborative systems. The outcomes indicate that the proposed system yields higher values in quality standards and minimizes the drawbacks of the traditional recommender system.

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