Sentiment analysis techniques in recent works

Madhoushi, Zohreh; Hamdan, Abdul Razak; Zainudin, Suhaila

doi:10.1109/sai.2015.7237157

Cited by 83 publications

(43 citation statements)

References 35 publications

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“…These have been resolved both at the lexical and syntactic level and many review works have been presented previously [36,37]. Recently developed techniques in the field of opinion mining and sentiment analysis still have drawbacks in solving multi-domain problems owing to the unavailability of sufficient labeled data [38][39][40].…”

Section: Sentiment Analysis and Lexicon Based Svm Classificationmentioning

confidence: 99%

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Shafqat

Byun

2019

Sustainability

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With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

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Section: Sentiment Analysis and Lexicon Based Svm Classificationmentioning

confidence: 99%

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Shafqat

Byun

2019

Sustainability

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“…Important supervised ML algorithms are Linear Classifiers (Support Vector Machine, Neural Network), Decision Trees, Rule-based and Probabilistic Classifier (Bayesian Network, Maximum Entropy, Naïve Bayes). Hereby, the biggest limitation is a high sensitivity to the quantity and quality of the training data, which can cause failure when training data is biased or insufficient [20]. This disadvantage is addressed by the unsupervised ML methods, such as LDA [6] or LSA [11], allowing to avoid the dependence on training the data.…”

Section: Machine Learning-based Sentimentmentioning

confidence: 99%

Business Sentiment Analysis. Concept and Method for Perceived Anticipated Effort Identification

Rizun

Revina

2019

SSRN Journal

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Representing a valuable human-computer interaction interface, Sentiment Analysis (SA) is applied to a wide range of problems. In the present paper, the researchers introduce a novel concept of Business Sentiment (BS) as a measurement of a Perceived Anticipated Effort (PAE) in the context of business processes (BPs). BS is considered as an emotional component of BP task contextual complexity perceived by a process worker after reading the task text. PAE is interpreted as a business process (BP) key performance indicator predicting urgency, criticality and complexity of the BP task processing. Using qualitative evaluation, the researchers proved the workability of both BS concept and its effective application method to measure PAE. As practical contributions of the research, quantitative support in a form of statistical reports and qualitative support in a form of task prioritization recommendations and time management for a BP worker are suggested.

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“…Other researchers have tried to define the scope by defining lists of verbs, adjectives and adverbs and defining their relationships for sentiment analysis . Lists of positive and negative terms and a set of lists for modifiers was proposed in [8] to define the scope of these modifiers as n-terms before and after positive or negative terms, although this n remained a constant. This technique is better for negation identification in comparison to the BOW technique.…”

Section: Bag Of Wordsmentioning

confidence: 99%

Comparitive Study on Sentiment Analysis Techniques and User Behavior Prediction on Twitter Data

Babu¹,

Paul²

2017

IJECS

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Abstract:People share knowledge, experiences and thoughts with the world by using Social Media like blogs, forums, wikis, review sites, social networks, tweets and so on. This has changed the manner in which people communicate and influence social, political and economic behavior of other people in the Web.This work mainly focus on different sentiment analysis techniques a comparative study of different sentiment analysis techniques and a proposed model that uses sentiment analysis on twitter data and user behavior prediction Proposed System mainly rely on Twitter Data.Performing sentiment analysis on twitter data and predicting the behavior of the tweets and thus the user who post those tweets. By analyzing tweets on certain groups the behavior of those groups can also be identified. Social networking websites are considered as major sources of opinions and views of the public on the prevalent social issues at a given point in time. Websites like the Twitter reflect the public views through its millions of messages posted by its users world wide,.By conducting survey these data set required for training data set is being created.and by using these training set twitter commands are analysed and thus the behavior of each tweets are extracted.Based on these tweets if any malicious activities are going on they can be detected and banned.

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Sentiment analysis techniques in recent works

Cited by 83 publications

References 35 publications

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Business Sentiment Analysis. Concept and Method for Perceived Anticipated Effort Identification

Comparitive Study on Sentiment Analysis Techniques and User Behavior Prediction on Twitter Data

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