Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

2021 ◽  
Vol 17 (2) ◽  
pp. 59-78
Author(s):  
Shailendra Kumar Singh ◽  
Manoj Kumar Sachan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Large Scale ◽  
English Language ◽  
Product Reviews ◽  
Internet Users ◽  
Social Media Text ◽  
Analysis System

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.

scholarly journals Polarity Classification Tool for Sentiment Analysis in Malay Language

2019 ◽  
Vol 8 (3) ◽  
pp. 259
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Ros Aziehan Rahmat ◽  
Umar Faruq Othman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Sentiment Lexicon ◽  
The Social ◽  
Polarity Classification ◽  
Media Channels ◽  
Classification Tool

<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.</p>

scholarly journals To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

2019 ◽  
Vol 8 (12) ◽  
pp. 4433-4435
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Social Media Text ◽  
The Impact

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

scholarly journals Sentiment Trend Analysis of Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 59-64
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Sources ◽  
Product Reviews

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

SentiVerb system: classification of social media text using sentiment analysis

2019 ◽  
Vol 78 (22) ◽  
pp. 32109-32136
Author(s):  
Shailendra Kumar Singh ◽  
Manoj Kumar Sachan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Text

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

scholarly journals A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

2018 ◽  
Vol 7 (2.21) ◽  
pp. 319
Author(s):  
Saini Jacob Soman ◽  
P Swaminathan ◽  
R Anandan ◽  
K Kalaivani
Keyword(s):  
Sentiment Analysis ◽  
Life Events ◽  
Social Networking Sites ◽  
Positive Emotions ◽  
English Language ◽  
Indian Languages ◽  
Regional Language ◽  
Comparative Review ◽  
Computational Research ◽  
Regional Languages

With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples’ emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.  

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

scholarly journals A Check on Annotation in Sentiment Research

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1346-1350
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Research Area ◽  
Research Literature ◽  
Manual Annotation ◽  
Automatic Annotation ◽  
Annotation Method ◽  
New Concepts

The research literature on sentiment analysis methodologies has exponentially grown in recent years. In any research area, where new concepts and techniques are constantly introduced, it is, therefore, of interest to analyze the latest trends in this literature. In particular, we have chosen to primarily focus on the literature of the last five years, on annotation methodologies, including frequently used datasets and from which they were obtained. Based on the survey, it appears that researchers do more manual annotation in the formation of sentiment corpus. As for the dataset, there are still many uses of English language taken from social media such as Twitter. In this area of research, there are still many that need to be explored, such as the use of semi-automatic annotation method that is still very rarely used by researchers. Also, less popular languages, such as Malay, Korean, Japanese, and so on, still require corpus for sentiment analysis research.

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