Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms

Abstract

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.

Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM

“…In order to demonstrate the performance of the proposed model, compare and analyze it with the models in references [ 13 , 14 ] and [ 17 ], optimize the training parameters of each comparative experiment for many times, and select the experimental data with the best effect. The statistics of the experimental results are shown in Table 2 .…”

“…Because the proposed model combines CNN and BiLSTM, it not only uses CNN to extract the deep features of the text, but also obtains the context information of the text based on BiLSTM, which is more conducive to the analysis of text emotion. Reference [ 13 ] implemented text emotion analysis based on dictionary model. Compared with deep learning algorithm, its analysis accuracy is low, lower than 0.80.…”

“…Reference [ 12 ] analyzed and expounded the level of emotion analysis, various emotion models, emotion analysis, and text emotion detection process, and discussed the challenges faced in the process of emotion analysis. Reference [ 13 ] proposed a dictionary-based text emotion analysis system. This method is the main standard method for modeling text for machine learning in emotion analysis methods, which can effectively solve the problem of data overload.…”

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

“…Sentiment analysis was also used in the work performed by Ajitha et al where they proposed a system using a lexicon-based model, which was able to classify the feelings of the customers based on their feedback taken from emails, tweets, call center, and surveys. The authors used a Machine Learning-based fusion technique that categorized the feedback as positive, negative, or neutral [ 101 ]. On the other hand, the methodology proposed by Abbasimehr and Shabani, used a time series forecasting component [ 102 ] that fuses the results of linear and non-linear models.…”

Hybrid neural network model based on multi-head attention for English text emotion analysis

“…More and more users are posting news or product reviews to express their opinions. Using emotion analysis technology to analyze these massive interactive information, users' emotional and psychological trajectories can be found to help research institutions grasp the dynamics of social emotions [1]. Text emotion analysis is to analyze, process, summarize and judge the emotion tendency of subjective and sexual text information with emotional color [2].…”