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 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

Deep Learning for Trilingual Character Recognition

2019 ◽  
Vol 8 (1) ◽  
pp. 52-58
Author(s):  
M. Yashodha ◽  
SK Niranjan ◽  
V. N. Manjunath Aradhya
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
English Language ◽  
The State ◽  
Corresponding States ◽  
National Language ◽  
Private Organizations ◽  
Regional Language ◽  
Multilingual Ocr ◽  
Regional Languages

As India is a multilingual country, in which the national language is Hindi, regional languages still exist in each of the corresponding states. In government offices, for the purpose of communication and maintenance of files and ledgers, the languages preferred are the regional languages and Hindi. As corporate offices and private organizations also exist in the country, these bodies mainly prefer the English language with the regional language in recording documents and ledgers. So, in this regard, in India a document contains multilingual texts, and there is a need of a multilingual OCR system. In this article, a trilingual OCR system is developed using deep learning for supporting English, Hindi and Kannada languages, the regional language of the state Karnataka.

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

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.

A Survey

2020 ◽  
Vol 11 (1) ◽  
pp. 28-33
Author(s):  
Reshma Radheshamjee Baheti ◽  
Supriya Kinariwala
Keyword(s):  
Sentiment Analysis ◽  
Life Events ◽  
Language Processing ◽  
Social Networking Sites ◽  
Human Life ◽  
Discussion Forums ◽  
Huge Number ◽  
Textual Data ◽  
Human Stress ◽  
Social Media Network

Recently, human stress is rapidly increasing. The school-college students, job professionals, and many people those work under pressure. In last few decades, research is going on how to predict people under pressure or feeling relax with his/her duty. In survey it is evaluated, sentiment analysis will work to find emotions or feelings about their daily life. By analyzing social media network like Facebook, Twitter, and other networking sites where user can share personal feelings like happy, angry, stressed, relaxed, or any other emotion to express human life events or views regarding any topic. On social networking sites, a huge number of informal messages are posted every day, also blogs or discussion forums are also available. Emotions appear to be frequently vital in these texts for expressing friendship, and the presentation of social support as a part of opinions or view. In this article, a survey is done on existing techniques which are working to find sentiment analysis of textual data. In the textual data, the positive and negative sentences have to be found to check the emotions of the user. The survey also finds the natural language processing, the lexical parser, sentiment analysis, the classifier algorithm and some different kinds of Twitter datasets. It is found that 85% work completed on sentiment analysis and categorized the sentences as positive or negative.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

A Review on Sentiment Classification: Natural Language Understanding

2019 ◽  
Vol 13 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Srishty Jindal ◽  
Kamlesh Sharma
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Natural Language Understanding ◽  
Business Analytics ◽  
Language Understanding ◽  
Text Data ◽  
Data Set ◽  
Market Positioning ◽  
Illegal Activities

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

scholarly journals Critical incidents in anorexia nervosa: perspectives of those with a lived experience

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jenni Leppanen ◽  
Lara Tosunlar ◽  
Rachael Blackburn ◽  
Steven Williams ◽  
Kate Tchanturia ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Lived Experience ◽  
Life Events ◽  
Emotional Processing ◽  
Positive Emotions ◽  
Online Survey ◽  
Disease Model ◽  
Positive Outcomes ◽  
Critical Events ◽  
Social Emotional

Abstract Background Although social-emotional difficulties are believed play a key role in anorexia nervosa (AN), there is uncertainty regarding what these difficulties might look like. Previous research has largely focused on a “disease model” of social-emotional processing in AN with little attention paid to positive emotions and experiences. Therefore, the aim of the present study was to obtain a fuller picture of critical life events as identified by those with lived AN experience. Methods Thirty-four participants aged 16–48 with current or past AN completed an online survey describing self-defined positive and difficult critical events. Thematic analysis was used to assess patterns in participants narrative responses. Results Two major themes were identified in the descriptions of positive critical events: Moments of celebration and Unexpected positive outcomes. These major themes revealed increased external focus and some corrective experiences that challenged the participants pre-existing expectations leading to new positive outcomes. Difficult events clustered into life events that were identified as Eating disorder (ED) related and Non-ED related and included the dimensions of relational conflict and feeling unsupported. Discussion The findings suggest that although negative emotionality was identified in the accounts of those with lived experience of AN capacity for “big-picture” thinking with and explicit focus on others was also identified. Moreover, an openness to corrective experiences that worked to challenge negative expectations was evident for some participants. Together these findings have scope as targets for further clinical research and treatment interventions.

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