Keyword Based Tweet Extraction and Detection of Related Topics

Benny, Amrutha; Philip, Mintu

doi:10.1016/j.procs.2015.02.032

Cited by 19 publications

(5 citation statements)

References 6 publications

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“…10 The trends of top topics that are generated by proposed method in each batch. [31] 1.530920 0.738440 1.134680404 Twevent [32] 1.721218 0.388564 1.05489122 HUPM [16] 1.729917 0.362058 1.045987555 HUPC [17] 1.520165 0.825380 1.172772534 [31] 0.497221 0.819564 0.618938 TweEvent [32] 0.620762 0.482383 0.542894 HUPM [16] 0.895330 0.299086 0.448387 HUPC [17] 0.941176 0.172414 0.291439 co-occurrence of words of posts is extracted. Then using this HWA, the calculation was performed.…”

Section: Discussionmentioning

confidence: 99%

Persian topic detection based on Human Word association and graph embedding

Ranjbar-Khadivi¹,

Akbarpour²,

Feizi‐Derakhshi³

et al. 2023

Preprint

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In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods.

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Section: Discussionmentioning

confidence: 99%

Persian topic detection based on Human Word association and graph embedding

Ranjbar-Khadivi¹,

Akbarpour²,

Feizi‐Derakhshi³

et al. 2023

Preprint

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“…This model aimed to detect emerging events that could affect a geographical region in a city, and also quantitatively estimated the impact of such an event on the population nearby the event location. Another study [11] proposed a model to detect related topics based on clustering performed over a TF-IDF representation of tweets. The clustering was done by including weights between words called Associative Gravity Force [12] (along with other measures of similarity between words, as well as ranking of words), that accounted for the frequency of pairs of words occurring together.…”

Section: Related Workmentioning

confidence: 99%

Characterization of citizens using word2vec and latent topic analysis in a large set of tweets

Vargas-Calderón

Camargo

2019

Cities

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With the increasing use of the Internet and mobile devices, social networks are becoming the most used media to communicate citizens' ideas and thoughts.This information is very useful to identify communities with common ideas based on what they publish in the network. This paper presents a method to automatically detect city communities based on machine learning techniques applied to a set of tweets from Bogotá's citizens. An analysis was performed in a collection of 2,634,176 tweets gathered from Twitter in a period of six months.Results show that the proposed method is an interesting tool to characterize a city population based on a machine learning methods and text analytics. reducing the digital gap in Colombia. For instance, from 2011 to 2014, several portions of the city have experienced an increase of up to 148.3% in homes with Internet connection [2]. By 2011, both Facebook and Twitter were the most popular social networks in Bogotá [1]. Moreover, 65.5% of Bogotá's Internet users used social networks by 2014 [2]. This study concentrates on Twitter because it allows its users to write posts with a length ranging from 1 to 140 characters 1 , making Twitter a tool for microblogging, a form of communication in which users express their opinion about several topics in short posts (tweets) [3].By only taking the users' posts and not the explicit relations between the users, our large data set is an interesting basis for testing unsupervised community detection methodologies. In fact, the only common factor that relates the users from our data set is that they are connected by geographical location.Such unsupervised community detection methodologies help to understand social phenomena that takes place in that geographical region and in a particular period of using data from any social network with no a priori knowledge of people's relations.With the objective of identifying the main topics treated by Bogotá's population on Twitter, as well as detecting possible communities, we collected tweets emitted from Bogotá. Then, a Word2Vec model [4] was built in order to represent the set of tweets corresponding to each user as a vector in a vector space.The gap statistic [5] was used to estimate the number of clusters that could be formed using these vectors. Finally, a frequency distribution of words was built for each cluster, so that each cluster could be identified by its most frequent words, which ultimately characterizes a topic.Our work acknowledges that online social networks constitute one of the main scenarios where people express their opinions, which makes them an outstanding source of information that allows the characterization of important topics for the citizenship. Therefore, we provide a robust method for studying 1 Data was collected before the change in the maximum tweet length to 280 characters

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“…The advantages of using the three features are explored and experimented to find events and categorize them successfully. Benny and Philip (2015) proposed a keyword-based event detection. The authors proposed two algorithms, topic detection using associative gravity force and topic clustering and tweet retrieval, to overcome the problem of wrong correlation of patterns.…”

Section: Physical Occurrences Detectionmentioning

confidence: 99%

Event detection from Twitter – a survey

Ramachandran

Parvathi

2018

IJWIS

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Purpose “What’s happening?” around you can be spread through the very pronounced social media to everybody. It provides a powerful platform that brings to light the latest news, trends and happenings around the world in “near instant” time. Microblog is a popular Web service that enables users to post small pieces of digital content, such as text, picture, video and link to external resource. The raw data from microblog prove indispensable in extracting information from it, offering a way to single out the physical events and popular topics prevalent in social media. This study aims to present and review the varied methods carried out for event detection from microblogs. An event is an activity or action with a clear finite duration in which the target entity plays a key role. Event detection helps in the timely understanding of people’s opinion and actual condition of the detected events. Design/methodology/approach This paper presents a study of various approaches adopted for event detection from microblogs. The approaches are reviewed according to the techniques used, applications and the element detected (event or topic). Findings Various ideas explored, important observations inferred, corresponding outcomes and assessment of results from those approaches are discussed. Originality/value The approaches and techniques for event detection are studied in two categories: first, based on the kind of event being detected (physical occurrence or emerging/popular topic) and second, within each category, the approaches further categorized into supervised- and unsupervised-based techniques.

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Keyword Based Tweet Extraction and Detection of Related Topics

Cited by 19 publications

References 6 publications

Persian topic detection based on Human Word association and graph embedding

Persian topic detection based on Human Word association and graph embedding

Characterization of citizens using word2vec and latent topic analysis in a large set of tweets

Event detection from Twitter – a survey

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