Survey on Sentiment Analysis: A Comparative Study

Chidananda, Himadri Tanaya; Sagnika, Santwana; Sahoo, Laxman

doi:10.5120/ijca2017912952

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Authors done a sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence [1] More details and survey papers see in [30,26,19,18].…”

Section: Word Embedding Modelmentioning

confidence: 99%

Comparative study on sentimental analysis using machine learning techniques

Enduri

Sangi

Anamalamudi

et al. 2023

Mehran Univ. res. j. eng. technol.

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With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.

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Section: Word Embedding Modelmentioning

confidence: 99%

Comparative study on sentimental analysis using machine learning techniques

Enduri

Sangi

Anamalamudi

et al. 2023

Mehran Univ. res. j. eng. technol.

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“…This study is associated to lot of research areas on opinion mining and sentiment analysis. Pang and Lee (2008), Liu and Zhang (2012), Assiri et al (2015), Ravi and Ravi (2015), Balazs and Velásquez (2016), Mohey El-Din and Hussein (2016) and Tanaya et al (2017) presented recent surveys about sentiment analysis and opinion mining techniques, applications and challenges. Earlier work has proposed approaches for identifying subjective text that contains opinion and separating it from the objective text that expresses real information (Wiebe, 2000;Hatzivassiloglou and Wiebe, 2000;Riloff and Wiebe, 2003) The approaches in opinion mining are of four categories: supervised, unsupervised, semi-supervised and hybrid.…”

Section: Introductionmentioning

confidence: 99%

Combining RSS-SVM with genetic algorithm for Arabic opinions analysis

Ziani

Azizi

Zenakhra

et al. 2019

IJISTA

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Due to the large-scale users of the Arabic language, researchers are drawn to the Arabic sentiment analysis and precisely the classification areas. Thus, the most accurate classification technique used in this area is the support vector machine (SVM) classifier. This last, is able to increase the rates in opinion mining but with use of very small number of features. Hence, reducing feature's vector can alternate the system performance by deleting some pertinent ones. To overcome these two constraints, our idea is to use random sub space (RSS) algorithm to generate several features vectors with limited size; and to replace the decision tree base classifier of RSS with SVM. Later, another proposition was implemented in order to enhance the previous algorithm by using the genetic algorithm as subset features generator based on correlation criteria to eliminate the random choice used by RSS and to prevent the use of incoherent features subsets.

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