An enhanced feature‐based sentiment analysis approach

Saeed, Nagwa M. K.; Helal, Nivin A.; Badr, Nagwa L.; Gharib, Tarek F.

doi:10.1002/widm.1347

Cited by 11 publications

(5 citation statements)

References 19 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Otherwise, the classification may result in incorrect sentiment. Saeed et al [19] enhanced the performance of sentiment classification by negation handling, emoticons detection, and removal of spam reviews. The authors used the apriori algorithm to extract the features from the reviews.…”

Section: Negation Detectionmentioning

confidence: 99%

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Tariq¹,

Masood²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model has been proposed. Owing to similitude with social media text, the movie reviews dataset has been used for the implementation. Supervised machine learning models namely Decision Tree, Support Vector Machine, and Naïve Bayes models have been implemented for the task of sentiment classification. We have compared the models of single-layer architecture with multi-tier model. The results of Multi-tier model have slight improvement over the single-layer architecture. Moreover, multi-tier models have better recall which allow our proposed model to learn more context. We have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information.

show abstract

Section: Negation Detectionmentioning

confidence: 99%

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Tariq¹,

Masood²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…It can be performed at a document level [10], [11], sentence level [12], [13], topic level and aspect (feature) level [14], [15]. It can further be categorized based upon the techniques used, such as, lexicon-based [16]- [18], featuresbased [10], [19]- [21], those using conventional machine learning approaches, i.e., Naive Bayes (NB), SVM [18], and unsupervised methods [14], and more recently deep learningbased sentiment analysis [12], [22]. A detailed description of techniques and approaches used to perform sentiment analysis is explored in the survey conducted in [23].…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

2020

View full text Add to dashboard Cite

Students' feedback is an effective mechanism that provides valuable insights about teachinglearning process. Handling opinions of students expressed in reviews is a quite labour-intensive and tedious task as it is typically performed manually by the human intervention. While this task may be viable for smallscale courses that involve just a few students' feedback, it is unpractical for large-scale cases as it applies to online courses in general, and MOOCs, in particular. Therefore, to address this issue, we propose in this paper a framework to automatically analyzing opinions of students expressed in reviews. Specifically, the framework relies on aspect-level sentiment analysis and aims to automatically identify sentiment or opinion polarity expressed towards a given aspect related to the MOOC. The proposed framework takes advantage of weakly supervised annotation of MOOC-related aspects and propagates the weak supervision signal to effectively identify the aspect categories discussed in the unlabeled students' reviews. Consequently, it significantly reduces the need for manually annotated data which is the main bottleneck for all deep learning techniques. A large-scale real-world education dataset containing around 105k students' reviews collected from Coursera and a dataset comprising of 5989 students' feedback in traditional classroom settings are used to perform experiments. The experimental results indicate that our proposed framework attains inspiring performance with respect to both the aspect category identification and the aspect sentiment classification. Moreover, the results suggest that the framework leads to more accurate results than the expensive and labour-intensive sentiment analysis techniques relying heavily on manually labelled data.

show abstract

“…Classification of sentiments at the sentence level categorizes sentences into positive or negative groups. Finally, the classification of sentiments at the feature level helps identify sentiments behind each word based on the elements and features found in the review sentence, for example, as per the studies conducted by [31,32]. One of the challenges in identifying the relationship of sentiment words with a feature is that a sentiment word can have different meanings depending on the circumstances and fields in which it has been used.…”

Section: Related Studiesmentioning

confidence: 99%

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Abdullah¹,

Abdullah²,

Sani³

2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages:(1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) preprocessing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

show abstract

An enhanced feature‐based sentiment analysis approach

Cited by 11 publications

References 19 publications

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Contact Info

Product

Resources

About