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Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

There exist distinctive words that are used to express same semantics and as a result of this it has become hard to quantify the exact matching of words. To deal with this issue, past investigations endeavored to ascertain a likeness between distinctive pair of words. Conventional methodologies for computing word similarity are based on repositories like WordNet. It is a manually created lexical database and it processes semantic connection between various words. However, WordNet is a universally useful asset but wide range of words are not present in it and furthermore there exist an issue of identifying the meaning of words. Implication of words are diverse in WordNet when we utilize it in a textual framework. There exists a need of the refined approach that can gauge words resemblance in light of their co-occurrence. In this examination, we proposed an approach that registers likeness in text particular words, with the assistance of literary substance of various posts on StackOverflow. Our proposed strategy figures out word similarities in text by ascertaining the weighted co-occurrence in view of Computing Term Cooccurrence (CTC) and SentiWordNet. The exploratory outcome demonstrates that our system proposed an arrangement of words that are identified with text data is exceptional. Moreover, when it was compared with WordNet-based strategy named as WordNetres, it results with better outcomes.

A Vehicle Routing Problem (VRP) is a Non-Polynomial Hard Category (NP-hard) problem in which the best set of routes for a convoy of vehicles is traversed to deliver goods or services to a known set of customers. In VRP, some constraints are added to improve performance. Some variations of VRP are Capacitated Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Stochastic Demands (VRPSD), Vehicle Routing Problem with Time Window (VRPTW), Dynamic Vehicle Routing Problem (DVRP), and Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) where vehicle and routes have multiple constraints. Swarm intelligence is a well-used approach to solve VRPs. Moreover, different hybrid combinations of global and local optimization techniques are also used to optimize the said problem. In this research, an attempt is made to solve CVRP with VRPSD by using two different hybridized population-based approaches, that is, the Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO). The experiments showed the accuracy of  the improved CVRP that is superior to one obtained by using other classical versions and better than the results achieved by comparable algorithms. Besides, this improved algorithm can also improve search efficiency.