Search result clustering for Thai Twitter based on Suffix Tree Clustering

Thaiprayoon, Santipong; Kongthon, Alisa; Palingoon, Pornpimon; Haruechaiyasak, Choochart

doi:10.1109/ecticon.2012.6254293

Cited by 4 publications

(9 citation statements)

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“…To the best our knowledge, there are only two publications which incorporate suffix trees into Twitter [6] [7]. Authors in [7] uses a different approach compared to our work and focus on hot topic detection by using temporal and regional features, while authors in [6] propose an adaptation of Suffix Tree Clustering algorithm [8] in Thai language. However, the direct adaptations of STC such as [6] is not suitable for English language as tweets are not suitable for clustering word-by-word.…”

Section: Contributionmentioning

confidence: 99%

“…Authors in [7] uses a different approach compared to our work and focus on hot topic detection by using temporal and regional features, while authors in [6] propose an adaptation of Suffix Tree Clustering algorithm [8] in Thai language. However, the direct adaptations of STC such as [6] is not suitable for English language as tweets are not suitable for clustering word-by-word. In this work, our main contribution is to provide an adaptation of a character-based suffix tree algorithm with a new heuristic function for optimization and merging of clusters for Twitter domain.…”

Section: Contributionmentioning

confidence: 99%

“…Lexical similarity between documents in Twitter is usually examined by using Named Entity Recognition (NER) [46] [47] [48]. There are other clustering approaches which use genetic algorithms [49] or word occurrences as a similarity measure [6]. The purpose of lexical similarity is to find similarities between documents without using the semantic context of words.…”

Section: Related Workmentioning

confidence: 99%

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Tweets on a tree: Index-based clustering of tweets

Erpam¹

2017

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Computer-mediated communication, CMC, is a type of communication that occurs through use of two or more electronic devices. With the advancement of technology, CMC has started to become a more preferred type of communication between humans. Through computer-mediated technologies, news portals, search engines and social media platforms such as Facebook, Twitter, Reddit and many other platforms are created. In social media platforms, a user can post and discuss his/her own opinion and also read and share other users' opinions. This generates a significant amount of data which, if filtered and analyzed, can give researchers important insights about public opinion and culture.Twitter is a social networking service founded in 2006 and became widespread throughout the world in a very short time frame. The service has more than 310 million monthly active users and throughout these users more than 500 million tweets are generated daily as of 2016. Due the volume, velocity and variety of Twitter data, it cannot be analyzed by using conventional methods. A clustering or sampling method is necessary to reduce the amount of data for analysis.To cluster documents, in a very broad sense two similarity measures can be used: Lexical similarity and semantic similarity. Lexical similarity looks for syntactic similarity between documents. It is usually computationally light to compute lexical similarity, however for clustering purposes it may not be very accurate as it disregards the semantic value of words. On the other hand, semantic similarity looks for semantic value and relations between words to calculate the similarity and while it is generally more accurate than lexical similarity, it is computationally difficult to calculate semantic similarity.In our work we aim to create computationally light and accurate clustering of short documents which have the characteristics of big data. We propose a hybrid approach of clustering where lexical and semantic similarity is combined together. In our approach, we use string similarity to create clusters and semantic vector representations of words to interactively merge clusters.Keywords: clustering, twitter, summarization, suffix tree, semantic relatedness, data mining $ This technical report is based on the thesis "Tweets on a tree: Index-based clustering of tweets".

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Section: Contributionmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Tweets on a tree: Index-based clustering of tweets

Erpam¹

2017

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“…The main assumption underlying our theory is that the price of the target is not expensive enough that the consumer would not be affected by the advertisement. Thus, when producing music concerts, the concerts producer can use our theory to estimate the rough number of tickets sold and reputation of the musician from concerts, TV appearances and news [1][2][3][27][28][29][30][31][32][33][34][35][36].…”

Section: Oncertsmentioning

confidence: 99%

“…In this paper, weshow that themathematical modelof thehitphenomenonisalso applicableto the predictionreputationofmusiciansin the(Englishspeaking) world, thebox officeof theconcert.We analyzed using the data of SNS (TOPSY PRO) the LADYGAGA has gained followers most Twitter in 2011. [27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Consideration of Reputation Prediction of Ladygaga Using the Mathematical Model of Hit Phenomena

Kawahata¹,

Genda²,

Ishii³

2014

IJMA

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Revenue Prediction of Music Concerts Using the Mathematical Model of Hit Phenomena

Kawahata

Genda

Ishii

2013

2013 International Conference on Biometrics and Kansei Engineering

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A mathematical model for the hit phenomenon in entertainment within a society is presented as a stochastic process of interactions of human dynamics. The calculations for the Japanese motion picture market based on to the mathematical model agree very well with the actual residue distribution in time. The world most popular rock band Coldplay is analyzed using the SNS data. L'Arc-en-Ciel of Japanese rock band and LADYGAGA are also analyzed using the data of SNS as well. The results agree very well, so that the mathematical theory for hit phenomena can be applied to the estimation of tickets sold for Music concerts.

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Search result clustering for Thai Twitter based on Suffix Tree Clustering

Cited by 4 publications

References 9 publications

Tweets on a tree: Index-based clustering of tweets

Tweets on a tree: Index-based clustering of tweets

Consideration of Reputation Prediction of Ladygaga Using the Mathematical Model of Hit Phenomena

Revenue Prediction of Music Concerts Using the Mathematical Model of Hit Phenomena

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