scholarly journals Lexicon-based sentiment analysis on movie review in the Gujarati language

2021 ◽  
Vol 4 (1) ◽  
pp. 63
Author(s):  
Parita Shah ◽  
Priya Swaminarayan
Keyword(s):  
Sentiment Analysis ◽  
Gujarati Language

scholarly journals Sentiment analysis on film review in Gujarati language using machine learning

2022 ◽  
Vol 12 (1) ◽  
pp. 1030
Author(s):  
Parita Shah ◽  
Priya Swaminarayan ◽  
Maitri Patel
Keyword(s):  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Word Level ◽  
Document Frequency ◽  
Accuracy Result ◽  
Gujarati Language

<span>Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.</span>

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.

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

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

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 Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

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