scholarly journals A Machine Learning Approach to Extract Opinions from Social Media Content

2018 ◽  
Vol 7 (4.5) ◽  
pp. 257
Author(s):  
Salina Adinarayana ◽  
E Ilavarasan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Opinion Mining ◽  
Media Content ◽  
Learning Approach ◽  
Mobile Environment ◽  
Classification Approach ◽  
Machine Learning Approach ◽  
Semantic Orientation ◽  
Feature Based

The Opinion Mining (OM) from mobile based social media content (SMC) is more challenging compared to topic-based mining, and it cannot be performed based on just examining the presence of single words in the text containing opinion expressions. Moreover, the existing systems of opinion   classification find that a large number of features that are not feasible for the mobile environment. The existing methods of OM in this mobile environment do not consider the semantic orientation of the SMC in the review. The proposed machine learning approach extends the feature-based classification approach to identify the orientation of the phrase on taking context into account to improve the accuracy.   

How Social Media Influencers Govern Sentiment Territory

2017 ◽  
Vol 8 (1) ◽  
pp. 49-60 ◽  
Author(s):  
Vala Ali Rohani ◽  
Shahid Shayaa ◽  
Ghazaleh Babanejaddehaki
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Pearson Correlation ◽  
Supervised Machine Learning ◽  
Media Content ◽  
Learning Approach ◽  
Test Results ◽  
Machine Learning Approach ◽  
Pearson Correlation Test ◽  
Sentiment Score

In present research, the authors examined how social media influencers affect the overall sentiment of a topic. To this end, they utilized supervised machine learning approach to develop SentiRobo for measuring the sentiment score of social media content. In the next stage, they studied social media datasets with 375,141 records in the education domain to investigate the correlation between social media topics and top authors' sentiment. The Pearson correlation test results revealed that top one percent of social media authors are enough to significantly influence the whole sentiment of each topic.

scholarly journals News Classification Using Machine Learning

2021 ◽  
Vol 9 (5) ◽  
pp. 23-27
Author(s):  
SHWETA MAHAJAN
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Performance Improvement ◽  
Vital Role ◽  
Learning Approach ◽  
Entertainment Education ◽  
Meaningful Information ◽  
Textual Data ◽  
Machine Learning Approach

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

scholarly journals Design of Hybrid Method for Automatic Sentiment Classification System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3616-3620
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Classification System ◽  
Scientific Community ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Learning Approach ◽  
Business World ◽  
Major Aspect ◽  
Machine Learning Approach

The Developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that focuses on opinions and sentiments as a major aspect of their central goal. This work introduces hybrid approach that includes lexicon based approach and machine learning approach for extracting aspects and sentiments

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

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