Sentiment Analysis of Persian Political Tweets Using ParsBERT Embedding Model with Convolutional Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

DEHGHANI MOHAMMAD; Yazdanparast Zahra

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

Download Full-Text

View:

Download:

Information Seminar Paper

Title

Sentiment Analysis of Persian Political Tweets Using ParsBERT Embedding Model with Convolutional Neural Network

Author(s)

DEHGHANI MOHAMMAD | Yazdanparast Zahra

Keywords

Sentiment Analysis

Natural Language Processing

Machine Learning

Deep Neural Networks

Abstract

Recently, social networks have experienced an exponential growth, providing online users with a venue for expressing and sharing their views in a variety of areas. A very popular social media platform for getting user feedback and collecting data is Twitter. Sentiment Analysis consists of extracting and analyzing opinions of people and can be used to predict tweet polarities. In this paper, we present a Sentiment Analysis methodology for Persian political tweets for the first time in order to assist Iranian politicians. We used two datasets of Persian political tweets with three and seven classes which are labeled according their content. This is the first study of this particular subfield of Persian tweets, so we are using a variety of encoding methods, including Bag-of-words, Word Embeddings, as well as neural methods such as Word2Vec, FastText, and ParsBERT Embeddings. We intend to use these techniques to implement Sentiment Analysis in closed domain political tweets using Machine Learning techniques such as Random Forests, Support Vector Machines, and Neural Networks. As a result of our comparisons, we found that CNN+BiLSTM using ParsBERT embeddings had higher robustness than other networks, scoring 0. 89 on Dataset 1 and 0. 71 on Dataset 2.

Cites

No record.

References

No record.

Cite

APA: Copy

DEHGHANI, MOHAMMAD, & Yazdanparast, Zahra. (). . . SID. https://sid.ir/paper/1046837/en

Vancouver: Copy

DEHGHANI MOHAMMAD, Yazdanparast Zahra. . . Available from: https://sid.ir/paper/1046837/en

IEEE: Copy

MOHAMMAD DEHGHANI, and Zahra Yazdanparast, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046837/en

Related Journal Papers