A Sentiment Analysis Model for Service Reviews Using Social Media as Opinion Source

2015 ◽  
Author(s):  
Bonaventure Kwitonda ◽  
Khwairakpam Dhanashree
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Model

scholarly journals Applying LSTM for Stock Price Prediction with Sentiment Analysis

2021 ◽  
Author(s):  
Alexandre Heiden ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Market Volatility ◽  
Analysis Model ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Social Media Platforms

Financial news has been proven to be valuable source of information for the evaluation of stock market volatility. Most of the attention has been given to social media platforms, while news from vehicles such as newspapers are not as widely explored. Newspapers provide, although in a smaller volume, more reliable information than social media platforms. In this context, this research aims to examine the influence of financial news within the stock price prediction problem, by using the VADER sentiment analysis model to process the news and feed the sentiments as a feature into a LSTM-based stock price prediction model, along with the historical data of the assets. Experiments indicate that the model has better results when the news’ sentiments are considered, and the model demonstrates potential to accurately predict stock prices up to around 60 days into the future.

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals Predicting Collective Synchronous State of Sentiments for Users in Social Media

2019 ◽  
Vol 38 (3) ◽  
pp. 687-704
Author(s):  
Nida Saddaf khan ◽  
Muhammad Sayeed Ghani
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Political Party ◽  
Temporal Analysis ◽  
Analysis Model ◽  
Synchronous State ◽  
Social Media Site ◽  
Proposed Model ◽  
Use Of Social Media ◽  
Insight Into

The increasing use of social media offers researchers with an opportunity to apply the sentiment analysis techniques over the data collected from social media websites. These techniques promise to provide an insight into the users’ perspectives on many areas. In this research, a sentiment analysis model is proposed based on HMC (Hidden Markov Chains) and K-Means algorithm to predict the collective synchronous state of sentiments for users on social media. HMC are used to find the converged state while K-Means is used to find the representative group of users. For this purpose, we have used data from a well-known social media site, Twitter, which consists of the tweets about a famous political party in Pakistan. The time series sequences of sentiments, of each user are passed on to the system to perform temporal analysis. The clustering with three and four number of clusters are found to be significant giving the representative groups. With three clusters, the representative group constitute of 82% of users and with four clusters, two representative groups are found having 45 and 36% of users. Analyzing these groups helps in finding the most popular behavior of users towards the concerned political party. Moreover, the groups perhaps tend to influence the opinion of other users in the network causing changes in their sentiments towards this party. The experimental results show that the proposed model has the power to distinguish behavior patterns of different individuals in a network.

An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM

2021 ◽  
pp. 1063293X2110314
Author(s):  
C Pretty Diana Cyril ◽  
J Rene Beulah ◽  
Neelakandan Subramani ◽  
Prakash Mohan ◽  
A Harshavardhan ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Modern Society ◽  
Support Vector ◽  
Analysis Model ◽  
Data Set ◽  
Twitter Data ◽  
Automated Learning ◽  
Sentiment Score ◽  
The One

The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

scholarly journals Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

2021 ◽  
Vol 18 (24) ◽  
pp. 13028
Author(s):  
Charlotte Roe ◽  
Madison Lowe ◽  
Benjamin Williams ◽  
Clare Miller
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Public Perception ◽  
Application Programming Interface ◽  
Vaccine Hesitancy ◽  
Analysis Model ◽  
Search Terms ◽  
Machine Learning Approach ◽  
Application Programming

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

scholarly journals Automatic Sentiment Analysis Model Creation using Multi Kernel Improved Extreme Learning Machine

2020 ◽  
Vol 8 (6) ◽  
pp. 2727-2735
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Extreme Learning Machine ◽  
Opinion Mining ◽  
Support Vector ◽  
Analysis Model ◽  
Feature Sets ◽  
Research Activities ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Recent research activities related to opinion mining, sentiment analysis and emotion detection from natural language texts are all under the umbrella of affective computation. There is now a huge amount of textual information on social media (for example, forums, blogs, and social media) about consumers' ideas about buying products and service experiences. Sentiment analysis or opinion mining is part of an investigation that analyzes people's thoughts and feelings from written text available online. In this paper, this work present a comprehensive experiment to evaluate the effectiveness of psychological and linguistic features in emotion classification. In this scheme, we used five broad categories of LIWC (namely, psychological processes, linguistic processes, punctuation, spoken categories and personal concerns) as feature sets. Five types of LIWCs and their group combinations were considered in the experimental analysis. To understand the predictive performance of various aspects of the engineering scheme, five controlled learning algorithms (namely, Naïve Bayes, support vector machines, Extreme Learning Machine, Kernel Extreme Learning Machine, Multi Kernel Extreme Learning Machine) and proposed Multi Kernel Improved Extreme Learning Machine (MKIELM) are used. Experimental results show that the ensemble feature sets provides a higher predictive effect than the individual set..

A 360-Degree View of a Hospital by Analysing Patient’s Online Reviews Using Fuzzy Sentiment Analysis

2021 ◽  
pp. 097206342110320
Author(s):  
Alekh Gour ◽  
Sony Kumari
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
New Delhi ◽  
Analysis Model ◽  
Medical Sciences ◽  
Medical College ◽  
Analysis Strategy ◽  
Social Media Platforms

Millions of people use Internet for developing new skills, booking online tickets, socialising, etc. Out of the sundry activities, giving online reviews by customers has become very customary these days and the fastest medium to make one’s voice heard. With the advent of analytics, more specifically, text mining, the online reviews of the customers have made a huge difference in shaping the future strategies of the companies and have also helped them to study the customer responses of their rivals. In an effort to help hospitals analyse the patient’s reviews present online on various social media platforms, this paper analyses the 659 reviews of people across the nation, on one of the best medical college and hospital of India, All India Institute of Medical Sciences, New Delhi. An attempt is made in this article to develop fuzzy sentiment analysis model with integration of naïve base classifier, which helps to analyse reviews of different hospitals and can come up with their own social media competitive analysis strategy. The results reveal the value text mining can bring to the table for any hospital and the immense business value that it holds.

scholarly journals Sentiment Analysis of Social Media via Multimodal Feature Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2010
Author(s):  
Kang Zhang ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Jianxin Liu ◽  
Wenxiao Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Feature Fusion ◽  
Image Features ◽  
Research Field ◽  
Sentiment Classification ◽  
Analysis Model ◽  
Noise Interference ◽  
Proposed Model ◽  
Multimodal Sentiment Analysis

In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies often ignore the interaction between text and images. Therefore, this paper proposes a new multimodal sentiment analysis model. The model first eliminates noise interference in textual data and extracts more important image features. Then, in the feature-fusion part based on the attention mechanism, the text and images learn the internal features from each other through symmetry. Then the fusion features are applied to sentiment classification tasks. The experimental results on two common multimodal sentiment datasets demonstrate the effectiveness of the proposed model.

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