What and With Whom? Identifying Topics in Twitter Through Both Interactions and Text

IEEE BASE PAPER ABSTRACT:

Twitter has become one of the most popular sources of real-time information about events happening in the world. Because of the overwhelming amount of information continuously flowing through the Twitter environment, topic derivation is essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such methods suffer from data sparsity. To alleviate this problem, this paper proposes a topic derivation method that incorporates tweet text similarity and interactions measures. Besides the tweet contents, the approach takes into account several types of interactions amongst tweets: tweets which mention the same people, replies and retweets. Topic derivation is done through a two-step matrix factorization process. We conducted a number of experiments on several Twitter datasets to reveal both the individual and integrated effects of the various features being considered. Our experimental results demonstrate that the proposed method outperforms other advanced topic derivation methods.

PROJECT OUTPUT VIDEO:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 2 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : JAVA.
Frontend : JSP, HTML, CSS, JavaScript.
IDE Tool : NetBeans IDE 8.2.
Database : MYSQL.

REFERENCE:

Robertus Nugroho, Jian Yang, Weiliang Zhao, Cecile Paris and Surya Nepal, “What and With Whom? Identifying Topics in Twitter Through Both Interactions and Text”, IEEE 2020.

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What and With Whom? Identifying Topics in Twitter Through Both Interactions and Text