scholarly journals An Empirical Comparison of Machine Learning Methods for Text-based Sentiment Analysis of Online Consumer Reviews

2021 ◽  
Author(s):  
Huwail J. Alantari ◽  
Imran S. Currim ◽  
Yiting Deng ◽  
Sameer Singh
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Empirical Comparison ◽  
Learning Methods ◽  
Consumer Reviews ◽  
Online Consumer Reviews ◽  
Machine Learning Methods ◽  
Online Consumer

scholarly journals Acoustic feature-based sentiment analysis of call center data

2017 ◽  
Author(s):  
◽  
Zeshan Peng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Call Center ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Acoustic Features ◽  
Learning Methods ◽  
Machine Learning Methods

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy.

Sentiment Analysis of Twitch.tv Livestream Messages using Machine Learning Methods

2021 ◽  
Author(s):  
Aryan Chouhan ◽  
Aayush Halgekar ◽  
Ashish Rao ◽  
Dhruvi Khankhoje ◽  
Meera Narvekar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

Machine Learning Approaches for Sentiment Analysis

2014 ◽  
pp. 193-208 ◽  
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

Machine Learning Approaches for Sentiment Analysis

Big Data ◽  
2016 ◽  
pp. 1917-1933
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

scholarly journals A Review on Multi-Lingual Sentiment Analysis by Machine Learning Methods

2020 ◽  
Vol 13 (2) ◽  
pp. 154-166 ◽  
Author(s):  
Santwana Sagnika ◽  
◽  
Anshuman Pattanaik ◽  
Bhabani Shankar Prasad Mishra ◽  
Saroj K. Meher
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Improving sentiment analysis for twitter data by handling negation rules in the Serbian language

2019 ◽  
Vol 16 (1) ◽  
pp. 289-311 ◽  
Author(s):  
Adela Ljajic ◽  
Ulfeta Marovac
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Learning Methods ◽  
Short Text ◽  
Machine Learning Methods ◽  
Relative Improvement ◽  
Twitter Data

The importance of determining sentiment for short text increases with the rise in the number of comments on social networks. The presence of negation in these texts affects their sentiment, because it has a greater range of action in proportion to the length of the text. In this paper, we examine how the treatment of negation impacts the sentiment of tweets in the Serbian language. The grammatical rules that influence the change of polarity are processed. We performed an analysis of the effect of the negation treatment on the overall process of sentiment analysis. A statistically significant relative improvement was obtained (up to 31.16% or up to 2.65%) when the negation was processed using our rules with the lexicon-based approach or machine learning methods. By applying machine learning methods, an accuracy of 68.84% was achieved on a set of positive, negative and neutral tweets, and an accuracy of as much as 91.13% when applied to the set of positive and negative tweets.

Public Sentiments Analysis Based on Fuzzy Logic for Text

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1341-1360 ◽  
Author(s):  
Xinzhi Wang ◽  
Hui Zhang ◽  
Zheng Xu
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Web Mining ◽  
Experimental Studies ◽  
Learning Technology ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Public Sentiment ◽  
Similar Accuracy

Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

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