Semantic Orientation-Based Approach for Sentiment Analysis

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.

Download Full-text

Machine Learning Approaches for Sentiment Analysis

Big Data ◽

10.4018/978-1-4666-9840-6.ch088 ◽

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.

Download Full-text

Impact of Deep Learning on Semantic Sentiment Analysis

Examining the Impact of Deep Learning and IoT on Multi-Industry Applications - Advances in Web Technologies and Engineering ◽

10.4018/978-1-7998-7511-6.ch007 ◽

2021 ◽

pp. 97-117

Author(s):

Neha Gupta ◽

Rashmi Agrawal

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Semantic Technologies ◽

Sources Of Information ◽

Accurate Analysis ◽

Online Social Media ◽

Semantic Orientation ◽

Semantically Enhanced

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.

Download Full-text

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Entropy ◽

10.3390/e22040473 ◽

2020 ◽

Vol 22 (4) ◽

pp. 473

Author(s):

Yongpeng Wang ◽

Hong Yu ◽

Guoyin Wang ◽

Yongfang Xie

Keyword(s):

Sentiment Analysis ◽

Latent Dirichlet Allocation ◽

Mapping Function ◽

Linear Mapping ◽

Feature Mapping ◽

Target Domain ◽

User Reviews ◽

Cross Domain ◽

Semantic Orientation ◽

Label Information

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

Download Full-text

Verb Based Sentiment Research

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1289.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 2468-2471

Keyword(s):

Sentiment Analysis ◽

Research Work ◽

Online Reviews ◽

Parts Of Speech ◽

Pos Tagging ◽

New Strategy ◽

Semantic Orientation ◽

The Difference ◽

Pos Tagger ◽

Speech Tagging

Sentiment Analysis is one of the leading research work. This paper proposes a model for the description of verbs that provide a structure for developing sentiment analysis. The verbs are very significant language elements and they receive the attention of linguistic researchers. The text is processed for parts-of-speech tagging (POS tagging). With the help of POS tagger, the verbs from each sentence are extracted to show the difference in sentiment analysis values. The work includes performing parts-of-speech tagging to obtain verb words and implement TextBlob and VADER to find the semantic orientation to mine the opinion from the movie review. We achieved interesting results, which were assessed effectively for accuracy by considering with and without verb form words. The findings show that concerning verb words accuracy increases along with emotion words. This introduces a new strategy to classify online reviews using components of algorithms for parts-of-speech..

Download Full-text

SEMANTIC GRAPH BASED TERM EXPANSION FOR SENTENCE-LEVEL SENTIMENT ANALYSIS

International Journal of Computing ◽

10.47839/ijc.19.4.2000 ◽

2020 ◽

pp. 647-655

Author(s):

Mohammed Maree ◽

Mujahed Eleyat

Keyword(s):

Sentiment Analysis ◽

Real World ◽

Gold Standard ◽

Semantic Networks ◽

Prototype System ◽

Analysis Techniques ◽

Negative Sentence ◽

Sentence Level ◽

Semantic Orientation

The semantic orientation (also referred to as prior polarity) of a word plays an important role in automatic sentence-level sentiment analysis. Several approaches have been proposed wherein a lexicon of words marked with their polarities is exploited to infer the meaning of sentences. However, relying on prior word polarity may produce inaccurate decisions. This is because we may find negative-sentence sentiments that include words with positive prior polarities or vice versa. In this article, we propose an approach to sentence-level sentiment analysis that exploits knowledge encoded in heavy-weight semantic graphs to assist in discovering the meaning of a word in the context of the sentence where it appears. In this context, we build contextual semantic networks for indexing sentences and expand them with semantically/lexically-relevant terms in an attempt to disambiguate the meanings of word mentions in sentences. In order to verify the effectiveness of the proposed approach, we have developed a prototype system using a real-world dataset that contains 46830 sentiment sentences along with a gold-standard that comprises 10000 movie reviews that are labelled under five sentiment categories (very negative, negative, neutral, positive, very positive). Findings indicate that enriching the semantic graphs of sentiment sentences with NOUN-based synonyms and hypernyms has improved the overall quality of baseline sentiment analysis techniques.

Download Full-text

Lexicon-Based Methods for Sentiment Analysis

Computational Linguistics ◽

10.1162/coli_a_00049 ◽

2011 ◽

Vol 37 (2) ◽

pp. 267-307 ◽

Cited By ~ 1181

Author(s):

Maite Taboada ◽

Julian Brooke ◽

Milan Tofiloski ◽

Kimberly Voll ◽

Manfred Stede

Keyword(s):

Sentiment Analysis ◽

Subject Matter ◽

Mechanical Turk ◽

Classification Task ◽

Main Subject ◽

Unseen Data ◽

Semantic Orientation ◽

Polarity Classification

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Download Full-text