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Research Topics based on Sentiment Analysis

Research Topics based on Sentiment Analysis

   In machine learning, sentiment analysis is a type of natural language processing refers that gives the opinion of the model based on the reviews and analyzes the texts for polarity from positive to negative. It belongs to the supervised classification algorithm and is mainly used in text classification. In general, the sentiment analysis approach involves Lexicon based approach, machine learning-based approach, and hybrid-based approach. Machine learning methods are popular and provide high precision. In machine learning-based classification, there are two sets of documents required, a training set and a test set, and the classifying reviews are based on the machine learning techniques. Some of the techniques are involved in sentimental analysis are Sentiment classification - classifying all the documents based on the opinion of a certain sample and performed at different levels such as document, sentence, and feature, Feature-based sentiment classification - It takes the opinion of certain sample data, and Opinion summarization - It is the process of selecting the important data points by rewriting a few of the original sentences from the reviews in the classic text summarization.

   The machine learning approach in sentiment analysis comprises supervised and unsupervised learning models. The most commonly used supervised algorithms for sentiment analysis are Support Vector Machine (SVM), Na├»ve Bayes (NB) Maximum Entropy (ME), Random Forest, and Decision Tree. The regularly used unsupervised machine learning algorithm are K-means and Apriori Algorithms. The most popular applications of sentiment analysis are monitoring market research, product analysis, reputation management, finance and stock monitoring, customer service, social media monitoring. A new emerging technique in machine learning for the field of sentiment analysis is transfer learning, which uses existing knowledge to resolve various domain problems and produces advanced prediction results. Future scopes of sentiment analysis are opinion spam detection and aggregation, statistical analysis approach for evaluation, and combination of different algorithms such as logistic regression and support vector machine for sentiment analysis.

   • Sentiment analysis and text-based analytics automatically analyze a large amount of available data and extract opinions that may help customers and organizations achieve their goals.

   • Sentiment analysis can complement other systems such as recommendation systems, information extraction, and question answering systems.

   • Sentiment analysis brings together various research areas such as natural language processing, data mining, and text mining, as they strive to integrate computational intelligence methods into their operations and attempt to shed more light on and improve their products and services.

   • With the advent of the Internet, Various survey tools have become more readily available, but obtaining accurate and relevant data from customer surveys is a significant challenge.

   • Due to the dynamic nature of sentiments, opinions change with changing competition, technology, use, and many others. The dynamic aspect of sentiment analysis becomes necessary to handle large data dynamically.