The Impact of applying Different Preprocessing Steps on Review Spam Detection

Etaiwi, Wael; Naymat, Ghazi Al

doi:10.1016/j.procs.2017.08.368

Cited by 62 publications

(29 citation statements)

References 4 publications

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“…Some of the aforementioned studies have proposed a spam reviews detection technique while working on the dataset “DOSC” such as Narayan et al (), Etaiwi and Awajan (), Mani et al () and Etaiwi and Naymat () while Vanta and Aono () proposed the technique while working on “YelpNYC” dataset and (Fontanarava et al, ) worked on “YelpZIP” dataset.…”

Section: Evaluation Results and Discussionmentioning

confidence: 99%

“…Etaiwi and Naymat () and Etaiwi and Awajan () presented a study that aims to evaluate the influence of applying different preprocessing steps on the performance of the detection of spam reviews. In this algorithm, three different preprocessing steps are considered, namely stemming, removal of punctuation marks, stemming, and stop words.…”

Section: Related Workmentioning

confidence: 99%

“…In this algorithm, three different preprocessing steps are considered, namely stemming, removal of punctuation marks, stemming, and stop words. Etaiwi and Naymat () extracted two types of properties from data namely bag of words and linguistic properties. Etaiwi and Awajan () extracted the properties namely bag of words and words count.…”

Section: Related Workmentioning

confidence: 99%

“…Second category studies which detected spam reviews only without considering their influence on the performance of sentiment classification are as follows: Etaiwi and Naymat (2017) and Etaiwi and Awajan (2017) presented a study that aims to evaluate the influence of applying different preprocessing steps on the performance of the detection of spam reviews. In this algorithm, three different preprocessing steps are considered, namely stemming, removal of punctuation marks, stemming, and stop words.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the aforementioned studies have proposed a spam reviews detection technique while working on the dataset "DOSC" such as Narayan et al (2018), Etaiwi and Awajan (2017), Mani et al (2018) and Etaiwi and Naymat (2017) while Vanta and Aono (2019) proposed the technique while working on "YelpNYC" dataset and (Fontanarava et al, 2017) worked on "YelpZIP" dataset. Narayan et al (2018) used different types of text related properties such as LIWC, Uni-gram, and the sentiment score of each review and reached an accuracy of 86.25%, a precision of 90.00% and a recall of 83.72%.…”

Section: B) State-of-the-art Techniques Comparisonmentioning

confidence: 99%

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An enhanced feature‐based sentiment analysis approach

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In the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews. This article is categorized under: Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning

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