Adapting sentiment lexicons to domain-specific social media texts

Deng, Shuyuan; Sinha, Atish P.; Zhao, Huimin

doi:10.1016/j.dss.2016.11.001

Cited by 116 publications

(62 citation statements)

References 25 publications

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“…A word may have variety sentiment values according to its POS, as the same word may play different roles according to its POS. For example, “good” is mostly convey positive feelings when it is used as an adjective but no positive or negative feelings when it is used as a noun [ 10 ]. Since the POS Tagger makes use of Penn POS Tag [ 18 ] to tag the word in the text and SWN has only four POS tags such as a, r, v, and n, we just consider the word with the four POS tags, and we need to turn tags of the words from Penn POS Tags to SWN POS Tags, corresponding relations of Penn POS Tags and SWN POS Tags are displayed in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

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Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias

Han

Zhang

et al. 2018

PLoS ONE

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Sentiment analysis is widely studied to extract opinions from user generated content (UGC), and various methods have been proposed in recent literature. However, these methods are likely to introduce sentiment bias, and the classification results tend to be positive or negative, especially for the lexicon-based sentiment classification methods. The existence of sentiment bias leads to poor performance of sentiment analysis. To deal with this problem, we propose a novel sentiment bias processing strategy which can be applied to the lexicon-based sentiment analysis method. Weight and threshold parameters learned from a small training set are introduced into the lexicon-based sentiment scoring formula, and then the formula is used to classify the reviews. In this paper, a completed sentiment classification framework is proposed. SentiWordNet (SWN) is used as the experimental sentiment lexicon, and review data of four products collected from Amazon are used as the experimental datasets. Experimental results show that the bias processing strategy reduces polarity bias rate (PBR) and improves performance of the lexicon-based sentiment analysis method.

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Section: Methodsmentioning

confidence: 99%

“…Sentiment lexicon is collection of words (or phrases) that convey feelings [ 1 ]. Each entry in the sentiment lexicon is associated with its sentiment orientation and strength [ 10 ]. Entries in the lexicon can be divided into three categories according to their sentiment orientations, such as positive, negative, and neutral.…”

Section: Related Workmentioning

confidence: 99%

Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias

Han

Zhang

et al. 2018

PLoS ONE

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“…Kamus lexicon terdiri dari banyak kata yang memiliki nilai positif dan negatif. Metode lexicon sudah banyak digunakan dalam banyak penelitian dikarenakan efektif [5].…”

Section: Pendahuluanunclassified

Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia

fatra

Hayatin²,

Aditya³

2020

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This study proposes a classification of public response to the government's decision to move the Indonesian capital using the lexicon method. The results of testing accuracy are measured using a confusion matrix. The data in this study use data from Twitter in the form of tweets. The data contains tweets of community responses to the decision to move the Indonesian capital. Data passes through 5 preprocessing processes, namely case folding, punctuation removal, stopword removal, stemming, and tokenizing. Lexicon is used because it produces good accuracy values. In this study also will look for a dictionary that has the best classification results. The results of this study show the results of a good classification by approaching the results by experts.

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“…The achievement of customer satisfaction is considered as vitally important, as the survival, continuity and financial performance of hotels are formed and determined by it (Deng, Sinha, & Zhao 2017). Customer satisfaction could be described as the end-state vision that hotels focus on pursuing, due to the long-lasting advantages, such as favorable reputation, customer loyalty along with sustainable rate of return (Liu & Jang, 2009).…”

Section: Customer Satisfaction and Customer Loyalty In The Hospitalitmentioning

confidence: 99%

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2020

JTHM

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Customer satisfaction and customer loyalty are considered as a confirmation of remarkable service provision towards the customers. The formation of both constructs is viewed as a vital element which leads to the reinforcement of competitive advantage for the hotels. Various academic viewpoints are available concerning the constructs of customer satisfaction and loyalty; nevertheless, most of them indicate an opposition towards their correlation. The aim of this research was to investigate the factors that reinforce the satisfaction and loyalty levels of resort tourists and identify whether or not the two constructs are interrelated. A quantitative research methodology was applied, with the use of close-ended paper questionnaires, which were administered to international tourists staying in resort hotels in the Municipality of Hersonissos, one of the most popular tourist destinations in northern Crete. Findings confirmed a positive correlation between customer satisfaction and customer loyalty. Data also showed that food and beverage quality, location and price are considered as the most significant factors that enhance satisfaction levels, whereas emotional experiences, loyalty programs, customer service quality, and customer satisfaction were identified to have a significant impact on the formation of customer loyalty.

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Adapting sentiment lexicons to domain-specific social media texts

Cited by 116 publications

References 25 publications

Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias

Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias

Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia

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