scholarly journals A Sentiment Analysis of Gujarati Text using Gujarati Senti Word Net

2019 ◽  
Vol 8 (9) ◽  
pp. 2290-2292
Keyword(s):  
Sentiment Analysis ◽  
English Language ◽  
Research Work ◽  
Vital Role ◽  
Product Reviews ◽  
Indian Languages ◽  
Lexical Resource ◽  
Resource Poor ◽  
Gujarati Language ◽  
Regional Languages

Sentiment Analysis plays vital role in decision making. For English language intensive research work is done in this area. Very less work is reported in this domain for Indian languages compared to English language. Gujarati language is almost unexplored for this task. More data in form of movie reviews, product reviews, social media posts etc are available in regional languages as people like to use their native language on Internet which leads to need of mining these data in order to understand their opinion. Various tools and resources are developed for English language and few for Indian languages. Gujarati is resource poor language for this task. Motive of this paper is to develop sentiment lexical resource for Gujarati language which can be used for sentiment analysis of Gujarati text. Hindi SentiWordNet (H-SWN) [1] and synonym relations of words from IndoWordnet (IWN) [2] [3] are used for developing Gujarati SentiWordNet. Our contribution is twofold. (1) Gujarati SentiWordNet (G-SWN) is developed. (2) Gujarati corpus is prepared in order to evaluate lexical resource created. Evaluation result shows the usefulness of generated resource

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 “What is Your Good Name?”: on Translating Multicultural Literature

2019 ◽  
Vol 16 (3) ◽  
pp. 406-414
Author(s):  
Anastasia S. Rogovets
Keyword(s):  
Language Contact ◽  
English Language ◽  
Multicultural Literature ◽  
World Englishes ◽  
Indian Languages ◽  
Indian English ◽  
Current Spread ◽  
Linguistic Transfer ◽  
Regional Languages

The article discusses distinguishing features of speech etiquette in Indian English and certain aspects of its translation into Russian. The relevance of this research topic is determined by the current spread of English as an international language and by the emergence of the World Englishes paradigm. In India there are a lot of cultural conventions that do not have English equivalents and, thus, cannot be expressed adequatelyby means of the English language. As a result of the language contact, Indian English has got an impact on its linguistic setting from Hindi and other regional languages. This linguistic transfer from Indian languages can be seen at various levels, including the use of politeness formulas. In this article the focus is made on the politeness formula “What is your good name?”, which is a polite way of asking someone’s name. This etiquette question is one of the most common Indian English politeness patterns, generalized all over India. The article analyzes the etymology of this expression and explains why it is frequently encountered in the speech of Indian English users, as well as to show the important role of such an analysis in overcoming translation difficulties.

scholarly journals Assorted Sentiment Model for Publically Available Page of Facebook

2020 ◽  
Vol 9 (3) ◽  
pp. 1160-1167
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
Business Strategy ◽  
English Language ◽  
Learning Algorithm ◽  
Indian Languages ◽  
Analysis Model ◽  
Indian Language ◽  
The Social ◽  
Bayes Algorithm

ecommerce industries expose public page in the social network site (Facebook, twitter etc) for the intention of improving of business strategy. They extract public mood about the social network page in the forms of total likes, the total share of the page and sentiment of all comments to the social network page similar way celebrities expose public page in the social network sites for the intention of improving its fame. We have developed an assorted model for publicly available page of Facebook. This assorted model is the combination of data extractor model, language convertor and cleaned model, and sentiment analyzer model. Our data extractor model extract comments on all the posts of publicly expose Facebook page in the less span of time. Language convertor and cleaned model would work for conversion of text written in different Indian language to the English language and after that English written text would be cleaned through cleaned model. Language convertor is made after implementing CILTEL model. CILTEL model converts comments written in the Indian languages in the English language. Cleaning model will clean all the comments of all the posts on the Facebook page. Finally, sentiment extraction model will extract sentiments of all the comments of the Facebook page. We have implemented classification using three machine learning algorithm, namely naïve bayes algorithm, perceptron algorithm and rocchio algorithm for checking the performance of our sentiment analysis model. Our assorted sentiment analysis model is beneficial to users like marketing industry, election parties and celebrities

scholarly journals Multi - Class Document Classification: Effective and Systematized Method to Categorize Documents

2020 ◽  
pp. 118-123 ◽  
Author(s):  
Kaushika Pal ◽  
Biraj V. Patel
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Nearest Neighbor ◽  
Research Work ◽  
Support Vector ◽  
Indian Languages ◽  
K Nearest Neighbor

A large section of World Wide Web is full of Documents, content; Data, Big data, unformatted data, formatted data, unstructured and unorganized data and we need information infrastructure, which is useful and easily accessible as an when required. This research work is combining approach of Natural Language Processing and Machine Learning for content-based classification of documents. Natural Language Processing is used which will divide the problem of understanding entire document at once into smaller chucks and give us only with useful tokens responsible for Feature Extraction, which is machine learning technique to create Feature Set which helps to train classifier to predict label for new document and place it at appropriate location. Machine Learning subset of Artificial Intelligence is enriched with sophisticated algorithms like Support Vector Machine, K – Nearest Neighbor, Naïve Bayes, which works well with many Indian Languages and Foreign Language content’s for classification. This Model is successful in classifying documents with more than 70% of accuracy for major Indian Languages and more than 80% accuracy for English Language.

A Comprehensive Guideline for Bengali Sentiment Annotation

2022 ◽  
Vol 21 (2) ◽  
pp. 1-19
Author(s):  
Md. Saddam Hossain Mukta ◽  
Md. Adnanul Islam ◽  
Faisal Ahamed Khan ◽  
Afjal Hossain ◽  
Shuvanon Razik ◽  
...  
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Web Mining ◽  
English Language ◽  
Research Work ◽  
Bengali Language

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

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.

Sentiment Analysis on Gujarati Text: A Survey

2020 ◽  
Vol 17 (9) ◽  
pp. 4075-4082
Author(s):  
Parita Vishal Shah ◽  
Priya Swaminarayan
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
English Language ◽  
Social Networking Site ◽  
Mobile Technologies ◽  
Huge Amount ◽  
Amount Of Information ◽  
Automated Method ◽  
World Information ◽  
Gujarati Language

Internet is a source of huge amount of information generated from blog, social websites, and forums and so on by user. In today’s world information available on the internet plays an important role in human’s life. To analyze a huge amount of information it’s require an automated method to classify this type of information. High usage of web and mobile technologies, user generated content in Guajarati is increasing on the web is motivation behind sentiment analysis. Emotion analysis is the process of identifying user’s opinion in section of text. This opinion helps to carry out decisions. Now a day’s a new source of opinion for users are web documents. Sentiment analysis is natural language processing task that extract information from various sources such as news, social networking site, blog, forums and classify them into positive, negative or neutral on the basis of their polarity. Lots of research is done in English language but it’s also important to perform sentiment analysis in Gujarati language as it is 6th official language in India. This paper gives an overview how sentiment analysis can be performed in Gujarati Language.

Exploiting Chi Square Method for Sentiment Analysis of Product Reviews

2020 ◽  
pp. 422-439
Author(s):  
Nilesh M Shelke ◽  
Shrinivas P Deshpande
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Research Work ◽  
Primary Objective ◽  
Product Reviews ◽  
Chi Square ◽  
Performance Matrix ◽  
Domain Independent

Sentiment analysis is an extension of data mining which employs natural language processing and information extraction task to recognize people's opinion towards entities such as products, services, issues, organizations, individuals, events, topics, and their attributes. It gives the summarized opinion of a writer or speaker. It has received lot of attention due to increasing number of posts/tweets on social sites. The proposed system is meant to classify a given text of review into positive, negative, or the neutral category. Primary objective of this article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews. Publicly available freely dictionary SentiWordNet 3.0 has been used for review classification. The proposed system is domain independent and context aware. Another objective of the proposed system is to identify the feature specific intensity with which reviewer has expressed his opinion. Effectiveness of the proposed system has been verified through performance matrix and compared with other research work.

scholarly journals Sentiment Analysis for Roman Urdu

2019 ◽  
Vol 38 (2) ◽  
pp. 463-470 ◽  
Author(s):  
Ayesha Rafique ◽  
Kamran Malik ◽  
Zubair Nawaz ◽  
Faisal Bukhari ◽  
Akhtar Hussain Jalbani
Keyword(s):  
Sentiment Analysis ◽  
Gradient Descent ◽  
English Language ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Online Comments ◽  
Better Than

The majority of online comments/opinions are written in text-free format. Sentiment Analysis can be used as a measure to express the polarity (positive/negative) of comments/opinions. These comments/ opinions can be in different languages i.e. English, Urdu, Roman Urdu, Hindi, Arabic etc. Mostly, people have worked on the sentiment analysis of the English language. Very limited research work has been done in Urdu or Roman Urdu languages. Whereas, Hindi/Urdu is the third largest language in the world. In this paper, we focus on the sentiment analysis of comments/opinions in Roman Urdu. There is no publicly available Roman Urdu public opinion dataset. We prepare a dataset by taking comments/opinions of people in Roman Urdu from different websites. Three supervised machine learning algorithms namely NB (Naive Bayes), LRSGD (Logistic Regression with Stochastic Gradient Descent) and SVM (Support Vector Machine) have been applied on this dataset. From results of experiments, it can be concluded that SVM performs better than NB and LRSGD in terms of accuracy. In case of SVM, an accuracy of 87.22% is achieved.

Exploiting Chi Square Method for Sentiment Analysis of Product Reviews

2018 ◽  
Vol 9 (2) ◽  
pp. 76-93
Author(s):  
Nilesh M Shelke ◽  
Shrinivas P Deshpande
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Research Work ◽  
Primary Objective ◽  
Product Reviews ◽  
Context Aware ◽  
Chi Square ◽  
Performance Matrix ◽  
Domain Independent

Sentiment analysis is an extension of data mining which employs natural language processing and information extraction task to recognize people's opinion towards entities such as products, services, issues, organizations, individuals, events, topics, and their attributes. It gives the summarized opinion of a writer or speaker. It has received lot of attention due to increasing number of posts/tweets on social sites. The proposed system is meant to classify a given text of review into positive, negative, or the neutral category. Primary objective of this article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews. Publicly available freely dictionary SentiWordNet 3.0 has been used for review classification. The proposed system is domain independent and context aware. Another objective of the proposed system is to identify the feature specific intensity with which reviewer has expressed his opinion. Effectiveness of the proposed system has been verified through performance matrix and compared with other research work.

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