The Role of Text Pre-processing in Opinion Mining on a Social Media Language Dataset

Santos, Fernando Leandro dos; Ladeira, Marcelo

doi:10.1109/bracis.2014.20

Cited by 20 publications

(10 citation statements)

References 7 publications

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“…O corpus originalé composto de 10.000 avaliações de aplicativos móveis publicadas na loja virtual Google Play Store 6 . As sentenças foram anotadas manualmente apenas para a identificação da polaridade [dos Santos and Ladeira 2014]. Nós consideramos um subgrupo de 1630 sentenças escolhidas aleatoriamente, igualmente distribuídas entre negativas e positivas [Stiilpen Júnior 2016].…”

Section: Coleção De Dadosunclassified

Investigando Features de Sentenças para Classificação de Subjetividade e Polaridade em Português do Brasil

Oliveira¹,

Melo²

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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Identificar sentenças subjetivas e calcular a polaridade destas sentenças são duas importantes tarefas de análise de sentimentos. Apesar de não serem problemas novos, a maioria das soluções são voltadas para o idioma inglês. Neste artigo, nós propusemos e avaliamos uma abordagem baseada em aprendizagem de máquina para lidar com as duas tarefas em português. Nós investigamos o uso de dois modelos de classificação e também propusemos um conjunto de características linguísticas do próprio texto. Nós experimentamos e avaliamos os métodos contra um representativo conjunto de baselines e em um conjunto diversificado de datasets. Nossa abordagem alcançou os melhores resultados nas duas tarefas e em todos os conjuntos de dados de teste.

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Section: Coleção De Dadosunclassified

Investigando Features de Sentenças para Classificação de Subjetividade e Polaridade em Português do Brasil

Oliveira¹,

Melo²

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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“…Santos and Ladeira [81], in their paper, analysed the impact of text preprocessing before analysing the data, and found that preprocessing improved the precision of POS tagger. Banerjee et al [43] used the NLP steps like stemming, tokenization, stop word removal and pruning to reduce the language space for finding the duplicate bug reports.…”

Section: Preprocessing Of Unstructured Datamentioning

confidence: 99%

Natural language processing in mining unstructured data from software repositories: a review

Gupta

2019

Sādhanā

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With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion.

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“…They stated that preprocessing and behavior of dataset has great impact on sentiment analysis accuracy. Dos Santos & Ladeira, M [21] performed experiments on Portuguese reviews for presenting the role of text preprocessing in sentiment analysis. www.ijacsa.thesai.org Additionally this research presented a large corpus of 759 thousands reviews as their contribution.…”

Section: Related Workmentioning

confidence: 99%

Normalization of Unstructured and Informal Text in Sentiment Analysis

Javed¹,

Kamal²

2018

ijacsa

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Sentiment Analysis is problem of natural language processing which deals with the extraction and analysis of public sentiments shared about target entities over microbloging websites. This field has gained great attention due to the huge availability of decision making textual contents. Sentiment Analysis has enormous application areas such as; Market Analysis, Service Analysis, Showbiz analysis, Movies, sports and even the popularity and acceptance rate of political policies can also be predicted via sentiment analysis systems. Although tremendous volume of opinionative text is available but it is unstructured and noisy due to which sentiment classifiers can't achieve good outcomes. Normalization is the process used to clean noise from unstructured text for sentiment analysis. In this study we have proposed a mechanism for the normalization of informal and unstructured text. Proposed mechanism is comprised of four essential phases; Noise Reduction, Part of Speech Tagging, Stop Word Removal stemming and Lemmatization. Numerous experiments are performed on twitter data set with unsupervised lexicons and dictionaries. Python and Natural language toolkit is used for performing all four essential steps. This study demonstrates that utilization and normalization of informal tokens in tweets improved the overall classification accuracy from 75.42 to 82.357.

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The Role of Text Pre-processing in Opinion Mining on a Social Media Language Dataset

Cited by 20 publications

References 7 publications

Investigando Features de Sentenças para Classificação de Subjetividade e Polaridade em Português do Brasil

Investigando Features de Sentenças para Classificação de Subjetividade e Polaridade em Português do Brasil

Natural language processing in mining unstructured data from software repositories: a review

Normalization of Unstructured and Informal Text in Sentiment Analysis

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