Dictionary-Based Sentiment Analysis Applied to a Specific Domain

Cruz, Laura M.; Ochoa, José Luís; Roche, Mathieu; Poncelet, Pascal

doi:10.1007/978-3-319-55209-5_5

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…(Cruz & Poncelet, n.d.) [19] constructed a sentiment dictionary based on words obtained from web pages and finding of correlation of the words are done by using AcroDef M13 and TrueSkill methods. For experimental two datasets are considered such as Agriculture and Movie tweets which includes 50 positive, 61 negative and 1000 positive and 1000 negative data respectively.…”

Section: IImentioning

confidence: 99%

An Enhanced Dictionary based approach for identify the biomedical entities from PubMed Articles

GOVINDARAJ,

2024

Preprint

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Data mining is characterized as a process of transforming data information into a human-comprehensible code format such as rules, formula, algorithm, and so on. Bioinformatics is developed to solve a biological problem by using data mining technique. Identifying biomedical domain entities is a difficult task and the enhanced model is used to classify the entities from biomedical literature full text articles in PubMed database. The enhanced model 3 stages namely pre-processing, identification of the entities using dictionary-based approach and verification, validation with benchmarking databases. The model, Disgenet and Pubtator are considered as the dictionary which is a benchmarking database. This approach defines entities using the en_ner_bionlp13cg_md model from spacy package. Experimental purposes, 99 full text articles related to Alzheimer’s disease are considered which are downloaded from NCBI. Finally, demonstrated that our enhanced model for dictionary based approach outperforms in aspects of accuracy, precision and retrieval value. The model achieved 82% accuracy overall. Compared to state-of -art method, model obtained the better accuracy. These results suggested that the enhanced model is obtained high performance for extracting biomedical entities from PubMed articles. The improvement mostly due to the dictionary because Disgenet and Pubtator are considered the dictionary.

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Section: IImentioning

confidence: 99%

An Enhanced Dictionary based approach for identify the biomedical entities from PubMed Articles

GOVINDARAJ,

2024

Preprint

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“…Thus, a specific dictionary models the SMS better than one adapted from other areas or than a generic one. Social media texts may contain opinions about several topics, but terms used to express opinions are usually specific and highly correlated to a particular domain [5] . Also, the list must be in the same language as the analysed text.…”

Section: Sentiment Analysis Backgroundmentioning

confidence: 99%

Sentiment analysis based on a social media customised dictionary

et al. 2021

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This article presents a methodology to classify the polarity of words from selected Tweets. Usually, social media sentiment (SMS) is lexically determined, manually or by machine learning. However, these methods are either slow or based on a pre-established dictionary, thus not providing a customised analysis. We propose a methodology that, after having mined the topic-related Tweets, filters relevant words based on the mean and standard deviation frequency in positive and negative market days to remove neutral terms. Subsequently, through an ad hoc perceptual mapping, we assign a polarity to the dataset. This method allows the building of a dictionary associated with the investor sentiment customised to that organisation. A practical application was carried out to test the proposed methodology. The results were significant and in line with the behavioural finance theory, confirming that irrational investor feelings—expressed via social media—drive a portion of asset prices. Results also confirm the investor asymmetric behaviour under gain or loss scenarios, with the latter generating more impact than the former because people are risk-averse. The proposed method is expected to identify patterns of behaviour in social media linked to market oscillations, thereby contributing to risk management and optimising decision-making in the stock market. The use of both statistical and perceptual map filters allows a specific asset dictionary to be built; Textual sentiment analysis based on social media; The proposed method efficiently overcomes generic dictionaries and language issues.

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“…The methodological approach considers the sentiment analysis as supervised or no supervised classification depending on what kind of documents, re-classified as positive and negative, are accessible. In the unsupervised approach, the dictionary methods are applied [27][28][29][30]. In the supervised approach, the algorithms of machine learning, such as artificial neural networks [31][32][33][34], Support Vector Machine [35] are used in order to find the dependencies between the features of the text excerpt and the opinion expressed in a document.…”

Section: Sentiment Analysis Of the Statementmentioning

confidence: 99%

Computer-aided therapeutic diagnosis for anorexia

Spińczyk

Bas

Dzieciątko³

et al. 2020

BioMed Eng OnLine

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Background: Anorexia nervosa is a clinical disorder syndrome of the wide spectrum without a fully recognized etiology. The necessary issue in the clinical diagnostic process is to detect the causes of this disease (e.g., my body image, food, family, peers), which the therapist gradually comes to by verifying assumptions using proper methods and tools for diagnostic process. When a person is diagnosed with anorexia, a clinician (a doctor, a therapist or a psychologist) proposes a therapeutic diagnosis and considers the kind of treatment that should be applied. This process is also continued during therapeutic diagnosis. In both cases, it is recommended to apply computeraided tools designed for testing and confirming the assumptions made by a psychologist. The paper aims to present the computer-aided therapeutic diagnosis method for anorexia. The proposed method consists of 4 stages: free statements of a patient about his/her body image, the general sentiment analysis of statement based on Recurrent Neural Network, assessment of the intensity of five basic emotions: happiness, anger, sadness, fear and disgust (using the Nencki Affective Word List and conversion of words to their basic form), and the assessment of particular areas of difficulties-the sentiment analysis based on the dictionary approach was applied. Results: The sentiment analysis of a document achieved 72% and 51% of effectiveness, respectively, for RNN and dictionary-based methods. The intensity of sadness (emotion) occurring within the dictionary method is differentiated between control and research group at the level of 10%. Conclusion: The quick access to the sentiment analysis of a statement on the image of patient's body, emotions experienced by the patient and particular areas of difficulties of people prone to the anorexia nervosa disorders, may help to establish the diagnosis in a very short time and start an immediate therapy. The proposed automatic method helps to avoid patient's aversions towards the therapy, which may include avoiding patient-therapist communication, talking about less essential topics, coming late for the sessions. These circumstances can guarantee promising prognosis for recovering.

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Dictionary-Based Sentiment Analysis Applied to a Specific Domain

Cited by 13 publications

References 14 publications

An Enhanced Dictionary based approach for identify the biomedical entities from PubMed Articles

An Enhanced Dictionary based approach for identify the biomedical entities from PubMed Articles

Sentiment analysis based on a social media customised dictionary

Computer-aided therapeutic diagnosis for anorexia

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