Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

Sharef, Nurfadhlina Mohd; Zin, Harnani Mat; Nadali, Samaneh

doi:10.3844/jcssp.2016.153.168

Cited by 30 publications

(13 citation statements)

References 112 publications

(155 reference statements)

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“…The classification algorithm utilizes these datasets to verify algorithm performance and to learn dataset. In particular, the training dataset is used in learning dataset while the testing dataset is used in verifying the performance of the algorithm (Sharef et al, 2016).…”

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

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Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals' opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

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Section: Sentiment Classification Techniquesmentioning

confidence: 99%

“…On the other hand, the unsupervised learning model does not utilize labelled dataset. It is trained using datasets involving a group of inputs (Sharef et al, 2016;Tramer et al, 2016).…”

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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“…Such information can be used as an early warning to the public and stakeholders who manage outbreaks. The use of big data is also a common practice in the business sector (Sharef, Zin and Nadali 2016). The use of big data has a high commercial value for entrepreneurs, where it is increasingly being relied on for product reviews, either through social media or reviews from application stores.…”

Section: Text Analysis Based On Big Datamentioning

confidence: 99%

Big Data-Based Peer-to-Peer Lending Fintech: Surveillance System through Utilization of Google Play Review

Pranata

Farandy

2019

SSRN Journal

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The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Working papers are subject to formal revision and correction before they are finalized and considered published.

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“…Feature types can be explicit or implicit; the explicit has four feature types: Syntactic, semantic, link-based and stylistic features, while the implicit focuses on semantic and linguistic rules (Sharef et al, 2016).…”

Section: Linguistic Featuresmentioning

confidence: 99%

Multimodal Sentiment Analysis: A Comparison Study

Jazyah

Hussien

2018

Journal of Computer Science

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Sentiments and emotions play a pivotal role in our daily lives. They assist decision making, learning, communication and situation awareness in human environments. Sentiment analysis is mainly focused on the automatic recognition of opinions' polarity, as positive or negative. Nowadays, sentiment analysis is replacing the old web based survey and traditional survey methods that conducted by deferent companies for finding public opinion about entities like products and services in order to improve their marketing strategy and product of advertisement, at the same time sentiment analysis improves customer service. Large number of videos is being uploaded online every day. Video files contain text, visual and audio features that complement each other. Multimodality is defined by analyzing more than one modality, Multimodal Sentiment Analysis refers to the combination of two or more input models in order to improve the performance of the analysis; a combination of text and audiovisual inputs is an example. The automatic analysis of multimodal opinion involves a deep understanding of natural languages, audio and video processing, whereas researchers are continuing to improve them. This paper focuses on multimodal sentiment analysis as text, audio and video, by giving a complete image of it and related dataset available and providing brief details for each type, in addition to that present the recent trend of researches in the multimodal sentiment analysis and its related fields will be explored.

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Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

Cited by 30 publications

References 112 publications

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Big Data-Based Peer-to-Peer Lending Fintech: Surveillance System through Utilization of Google Play Review

Multimodal Sentiment Analysis: A Comparison Study

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