Generating Domain-Specific Sentiment Lexicons for Opinion Mining

Salah, Zaher; Coenen, Frans; Grossi, Davide

doi:10.1007/978-3-642-53914-5_2

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…They demonstrate that text mining techniques perform with comparable accuracy as traditional approaches in the generation of sentiment scores. Salah et al (2013) propose two approaches to generating domain-specific sentiment lexicons, namely, direct generation and domain adaptation. The first generates a dedicated lexicon from the labelled source data, while the second uses a general purpose sentiment lexicon and adapts it into a domain-sensitive lexicon on a particular domain.…”

Section: Domain Adaptationmentioning

confidence: 99%

Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

Darwich¹,

Noah²,

Omar³

et al. 2019

Journal of Digital Information Management

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State-of-the-art sentiment analysis systems rely on a sentiment lexicon, which is the most essential feature that drives their performance. This resource is indispensable for, and greatly contributes to, sentiment analysis tasks. This is evident in the emergence of a large volume of research devoted to the development of automated sentiment lexicon generation algorithms. The task of tagging subjective words with a semantic orientation comprises two core approaches: dictionarybased and corpus-based. The former involves making use of an online dictionary to tag words, while the latter relies on co-occurrence statistics or syntactic patterns embedded in text corpora. The end result is a linguistic resource comprising a priori information about words, across the semantic dimension of sentiment. This paper provides a survey on the most prominent research works that utilize corpus-based techniques for sentiment lexicon generation. We also conduct a comparative analysis on the performance of state-of-the-art algorithms proposed for this task, and shed light on the current progress and challenges in this area.

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Section: Domain Adaptationmentioning

confidence: 99%

Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

Darwich¹,

Noah²,

Omar³

et al. 2019

Journal of Digital Information Management

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“…In the corpus-based method, the pattern of word co-occurrence is considered, or new lexical resources are generated (Church and Hanks, 1990;Turney, 2002;Turney and Litman, 2003 (Du et al, 2010;Salah et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Bankruptcy Prediction Modeling Using Qualitative Information Based on Big Data Analytics

Jo¹,

Shin²

2016

Journal of Intelligence and Information Systems

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․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information.Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data.This study proposes a bankruptcy prediction model for Korean small-and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We empl...

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“…Manually constructed sentiment lexicons of relatively modest size may be expanded by beginning with a core collection of positive and negative seed words. This list is augmented via lexical initiation approaches that use the semantic links between words and their substitutes and antonyms or through term similarity measures in big corpora [3].…”

Section: Introductionmentioning

confidence: 99%

“…A general purpose sentiment lexicon can be converted into a dedicated lexicon by merging the relations between terms and opinion phrases to determine the most likely polarity of a term as positive, negative, or neutral in the given domain [42]. Two methodologies have also been published in other study [3], which involves adding new domain terms to the seed lexicon and extending it by changing the sentiment ratings of the phrases in it. By crowdsourcing assessment of sentiment phrases and extending the initial seed vocabulary automatically by bootstrapping to integrate new sentimental indicators and concepts, they constructed a domain-specific sentiment lexicon [43].…”

mentioning

confidence: 99%

Patient-patient interactions visualization for drug side effects in patients’ reviews

Salah,

Elsoud,

Salah

et al. 2024

IJEECS

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This paper describes the patient-patient interactions (PPIs) graph extraction framework from patient’s review transcripts. The concept is to visualise patients as nodes and interactions representing links. Links are made based on review text similarity. Nodes are categorized as positive or negative according to the patient’s attitude toward a given drug. Attitudes are then utilized to categorize the links as supporting or opposing the use of a certain drug. If both patients share the same attitude: negative (severe side effect) or positive (moderate side effect), the relationship is considered supportive; if not, the link is considered opposed. Resulting graph represent a drug as a dispute between two factions arguing on related drug. The framework is explained and evaluated using a dataset included 3,763 patients’ reviews linked to 255 different drugs, -predictive-value (0.37). We argue that, this is caused by derogatory jargon that is an expected feature of patient’s review. The true-negative-recognition-rate is 0.70 and true-positive-recognition-rate is 0.54. Total-average-accuracy, which is independent of class priors, is 0.66. Results show that, it is possible to use text proximity measures and sentiment analysis to capture PPIs structure.

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Generating Domain-Specific Sentiment Lexicons for Opinion Mining

Cited by 6 publications

References 13 publications

Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

Bankruptcy Prediction Modeling Using Qualitative Information Based on Big Data Analytics

Patient-patient interactions visualization for drug side effects in patients’ reviews

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