On the difficulty of clustering company tweets

Perez-Tellez, Fernando; Pinto, David; Cardiff, John; Rosso, Paolo

doi:10.1145/1871985.1872001

Cited by 22 publications

(7 citation statements)

References 9 publications

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“…They tested their application with the WePS-3 Online Reputation Management Corpus and obtained 0.69 accuracy. In [13], company tweets in WePS-3 are clustered as true or false according to the term expansion methodology. In order to solve company name ambiguity, they employed a clustering technique different from our classification task.…”

Section: Company Disambiguationmentioning

confidence: 99%

Exploring the power of supervised learning methods for company name disambiguation in microblog posts

Polat¹,

Çakmak²,

Nuray-Turan³

2020

Turk J Elec Eng & Comp Sci

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Twitter is an online social networking website where people can post short messages on any subject, and these messages become visible to other users. Users intentionally express their opinions about companies or products via microblogging texts. Analyzing such messages might help explore what customers think about company products, or what the broad feelings of customers are. Identifying tweets referring to products and companies is becoming an important tool recently. However, company names are often vague. Hence, the first step is to locate the messages that are relevant to a company. In this paper, we present a number of supervised learning techniques to decide whether a given tweet is about a company, e.g., whether a message containing the term 'amazon'is related to the company Amazon Inc. or not. Solving this task is challenging in comparison to the classical classification process. The main difficulty with this problem is that tweets and company names include limited information. To make this task tractable, external resources are used to get richer data about a company. More specifically, we generate several profiles for each organization, which contain richer information. Then we perform feature extraction to obtain both numerical and categorical features and we do feature selection to identify the most relevant attributes with our task. Finally, we train several supervised classifiers. Our constructed classifiers and carefully selected features provide high accuracy on the WePS-3 dataset. Our results show considerable improvement of accuracy by 11% over baseline approaches.

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Section: Company Disambiguationmentioning

confidence: 99%

Exploring the power of supervised learning methods for company name disambiguation in microblog posts

Polat¹,

Çakmak²,

Nuray-Turan³

2020

Turk J Elec Eng & Comp Sci

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“…Having a pre-processed subjective text with class labels, sentiment classification can be conducted at the document [13], sentence [14] or phrase levels [15] (where a phrase is part of a sentence) which we refer to as the granularity of the classification. Finally, knowing the source and the target of a sentiment is considered as one of the challenges of sentiment analysis that was addressed by number of researchers [16].…”

Section: Sentiment Analysismentioning

confidence: 99%

A Comparative Analysis of Machine Learning Classifiers for Twitter Sentiment Analysis

Ismail¹,

Harous²,

Belkhouche³

2016

RCS

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Twitter popularity has increasingly grown in the last few years making influence on the social, political and business aspects of life. Therefore, sentiment analysis research has put special focus on Twitter. Tweet data have many peculiarities relevant to the use of informal language, slogans, and special characters. Furthermore, training machine learning classifiers from tweets data often faces the data sparsity problem primarily due to the large variety of Tweets expressed in only 140-character. In this work, we evaluate the performance of various classifiers commonly used in sentiment analysis to show their effectiveness in sentiment mining of Twitter data under different experimental setups. For the purpose of the study the Stanford Testing Sentiment dataset STS is used. Results of our analysis show that multinomial Naïve Bayes outperforms other classifiers in Twitter sentiment analysis and is less affected by data sparsity.

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“…Another study exploredhow Twitter can be used to construct a news processing system, from tweets by automatically grouping news tweets into clusters, such that each cluster consists of tweets relating to a particular topic (Sankaranarayanan et al, 2009). Perez-Tellez et al (2010), presented and compared a number of different methods based on clustering to determine whether a certain tweet refers to a specific company or not.Application ofk-means clustering technique for masses consisting of a huge number of documents came up with the conclusion that when the documents' content is very short (as in the case of tweets), it is more appropriate to cluster the words instead of the documents. Therefore, a method that clusters the words using the word co-occurrence as a similarity measure was proposed by Khot (2010).…”

Section: Related Workmentioning

confidence: 99%

“…Karandikar (2010) stated "Such a short piece of text provides very few contextual clues for applying machine learning techniques". This type of data results in weak performance of most clustering methods due to the informal writing style of tweets that can be full of jargons, misspellings, colloquial and out of vocabulary words with poor grammatical structure (Perez-Tellez et al, 2010).…”

Section: The Problemmentioning

confidence: 99%

Clustering Tweets Using Cellular Genetic Algorithm

Adel¹

2014

Journal of Computer Science

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As the popularity of Twitter continues to increase rapidly, it is extremely necessary to analyze the huge amount of data that Twitter users generate. A popular method of tweet analysis is clustering. Because most tweets are textual, this study focuses on clustering tweets based on their textual content similarity. This study presents tweet clustering using cellular genetic algorithm cGA. The results obtained by cGA are compared with those obtained by generational genetic algorithm in terms of average fitness, average time required for execution and number of generations. Experimental results are tested with two sets: One of 1000 tweets and the second formed of 5000 tweets. The results show a nearly equal performance for both algorithms in terms of the average fitness of the solution. On the other hand, cGA shows a much faster performance than generational. These results demonstrate that cellular genetic algorithm outperforms generational genetic algorithm in tweet clustering.

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On the difficulty of clustering company tweets

Cited by 22 publications

References 9 publications

Exploring the power of supervised learning methods for company name disambiguation in microblog posts

Exploring the power of supervised learning methods for company name disambiguation in microblog posts

A Comparative Analysis of Machine Learning Classifiers for Twitter Sentiment Analysis

Clustering Tweets Using Cellular Genetic Algorithm

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