A Review of Sentiment Semantic Analysis Technology and Progress

Wang, Yiming; Rao, Yuan; Wu, Lianwei

doi:10.1109/cis.2017.00105

Cited by 11 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to three information evaluation metrics and much literature [28][29][30] , we divide information into five types -true information [10] , rumors [11] , biases [12] , fake news and spams [31,32] .…”

Section: The Types Of Informationmentioning

confidence: 99%

A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media

Rao

et al. 2019

Chinese Journal of Electronics

Self Cite

View full text Add to dashboard Cite

Section: The Types Of Informationmentioning

confidence: 99%

A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media

Rao

et al. 2019

Chinese Journal of Electronics

Self Cite

View full text Add to dashboard Cite

“…Typically, analysis takes place at an aspect, sentence, or document level, however a combination of levels is an additional possibility and at times -can offer a more comprehensive perspective. Successful sentiment analysis would be the precise extraction of the emotions surrounding a viewpoint within the context in which correct interpretation was intended by the author [7].…”

Section: Introductionmentioning

confidence: 99%

A Prediction of South African Public Twitter Opinion using a Hybrid Sentiment Analysis Approach

Shackleford,

Adeliyi,

Joseph

2023

IJACSA

View full text Add to dashboard Cite

Sentiment analysis, a subfield of Natural Language Processing, has garnered a great deal of attention within the research community. To date, numerous sentiment analysis approaches have been adopted and developed by researchers to suit a variety of application scenarios. This consistent adaptation has allowed for the optimal extraction of the authors emotional intent within text. A contributing factor to the growth in application scenarios is the mass adoption of social media platforms and the bondless topics of discussion they hold. For government, organizations and other miscellaneous parties, these opinions hold vital insight into public mindset, welfare, and intent. Successful utilization of these insights could lead to better methods of addressing said public, and in turn, could improve the overall state of public well-being. In this study, a framework using a hybrid sentiment analysis approach was developed. Various amalgamations were createdconsisting of a simplified version of the Valence Aware Dictionary and sEntiment Reasoner (VADER) lexicon and multiple instances of classical machine learning algorithms. In this study, a total of 67,585 public opinion-oriented Tweets created in 2020 applicable to the South African (ZA) domain were analyzed. The developed hybrid sentiment analysis approaches were compared against one another using well known performance metrics. The results concluded that the hybrid approach of the simplified VADER lexicon and the Medium Gaussian Support Vector Machine (MGSVM) algorithm outperformed the other seven hybrid algorithms. The Twitter dataset utilized serves to demonstrate model capability, specifically within the ZA context.

show abstract

“…It is also referred to as opinion mining as it derives an opinion of the user. Opinions vary from user to user, and sentiment analysis much helps in understanding users’ perspective [2 ].…”

Section: Introductionmentioning

confidence: 99%

Hadoop framework for efficient sentiment classification using trees

Sridharan

Komarasamy²,

Raja³

2020

IET Networks

View full text Add to dashboard Cite

Due to the increase in the speed of generation of data, the authors are forced to handle a massive volume of data with the help of conventional machine learning algorithms. Big data is an enormous volume of data which is beyond the capacity of the traditional database software tool to collect, store, manage, and process within a stipulated time limit. Sentiment analysis is analysing the data by classifying the text on the basis of strength and polarity of opinion (positive/negative) words that define the text. While handling big data, Hadoop provides a platform for users to develop their own sentiment analysis with the help of a lexicon dictionary or available application programming interface (API) or external programs. The aim of classifying data is to analyse extensive data and develop an appropriate description or model for every organised class with the feature present in the data. In this work, the feature extraction based on term frequency‐inverse document frequency is utilised and the Hadoop framework in attaining a useful classification with the help of random forest techniques.

show abstract

A Review of Sentiment Semantic Analysis Technology and Progress

Cited by 11 publications

References 18 publications

A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media

A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media

A Prediction of South African Public Twitter Opinion using a Hybrid Sentiment Analysis Approach

Hadoop framework for efficient sentiment classification using trees

Contact Info

Product

Resources

About