Sentiment Analysis over Social Networks: An Overview

Ahmed, Kawsar; El-Tazi, Neamat; Hossny, Ahmad Hany

doi:10.1109/smc.2015.380

Cited by 52 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They have used senti-wordnet to predict the score by determining the polarity of the sentence. Ahmed et al (2015) [22] have surveyed on tool enhancement methods and tools available for analysis. Though the analysis of the applications is done, the comparative approaches for these applications are not accomplished.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Priya¹,

Rithika²

2021

CVRJST

View full text Add to dashboard Cite

Mining online reviews to extract opinion targets and words are substantial tasks in fine-grained opinion mining. The main aim is to mine sensible multi-grain aspects and opinion words from unlabeled reviews. In this paper, Combined Aspect based Sentiment Model (CASM) is propounded to cooperatively mine multi-grain features and opinions. CASM deals with aspects, opinions, sentiment polarity and granularity concurrently. Support Vector Machine-Radial Basis Function kernel (SVM-RBF) classifier is applied to improve CASM by splitting aspects and opinion words. CASM-SVM-RBF deals with two different kinds of aspects and opinions: general and particular aspects, overall opinions and aspect-precise opinions. Candidates with improved confidence are mined as opinion targets or words. The performance is analyzed in terms of Accuracy, Precision, Recall and F-Score for KNN, BPNN, NBC and ME classifiers.

show abstract

Section: Related Workmentioning

confidence: 99%

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Priya¹,

Rithika²

2021

CVRJST

View full text Add to dashboard Cite

show abstract

“…These levels are sentence, status, document and user level, respectively. Different methods can be applied within these four levels, such as machine learning, lexicon, NLP, ontological or hybrid solutions [1].…”

Section: Related Workmentioning

confidence: 99%

Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

Müngen

Aygün

Kaya

2020

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

Nowadays, social media and online sharing sites are frequently used to share thoughts about daily events. Thanks to the posts made by internet users on these platforms, first, quite big data is generated to interpret the agenda. More than 10,000 comments of more than 5000 users made about COVID-19 from online websites between 15 March and 15 May were collected in this study. Then, emotional analysis on these comments was carried out with BERT, GRU, LSTM and TF-IDF methods. The changes in the amount of user comments and the emotions reflected by the comments have been associated with the actual events of these dates. It has been determined which types of events affect users more. In addition, the emotional response changes of the users to the official COVID-19 statistics were measured and the peak points of the emotional changes were determined. Finally, the emotion classification methods applied were evaluated by user questionnaires and their successes were determined according to F-Measure.

show abstract

“…Furthermore, users can do a lot of spelling mistakes while tweeting. Tweets can contain grammatical errors, hashtags, numerals, shortcut words, emoticons etc [11]. All these things may not be beneficial for analytics of data, we have to focus on following points:…”

Section: Emotion Detectionmentioning

confidence: 99%

Emotion detection on cross platform languages

Arif

Singh

Boparai

2018

IJET

View full text Add to dashboard Cite

Internet has changed the course of our living. It has become the most beneficial antecedent or source of information. Today almost everything is found on internet. Everyday millions of people post their ideas, reviews, stories about the services, products or other persons. The size of data is increasing tremendously. It is very difficult to analyze that amount of data and figure out the emotions or sentiments posed by people. Emotion detection is such a technique where we can judge people’s ideas and extract the emotion towards an entity or service. We have used subjective lexicon-based approach to bench the emoticons expressed by the ideas of the people. The data set that we have mainly focused is very cross and noisy. We have used Facebook data in Urdu and Kashmiri language. Both languages are very cross domain. These languages can be written in English alphabet that makes them more challenging to analyses. Our approach resolves the challenge to the maximum possible way. The results shown by our method on this kind of data set are better than any other approach. Our analysis on this type of dataset will help the local businessmen of these areas to grow and flourish. The analysis will give some insights what the local feel about the entity or product so that the manufacturers can design or build it that way and try to enhance its qualities.

show abstract

Sentiment Analysis over Social Networks: An Overview

Cited by 52 publications

References 38 publications

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

Emotion detection on cross platform languages

Contact Info

Product

Resources

About