# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Transformation
#### 3.1.3 Feature Engineering Using Spammer Behavioral Features Analysis  
The spammer behavioral features deal with metadata of the review rather than the text content of the review [34]. Table 9 shows different characteristics of reviews: review ID, reviewer ID, product ID, rating, useful, review word count, review date, and time. These review characteristic features may help in spam review detection. It was observed from the literature review that the researchers used reviewer's features [28; 35; 36] to identify the spammer. Moreover, the spammer may share common features, such as activity pattern, IP address, geographical location, and profile characteristics. With the help of these features or a combination of these features, spammer, spam, and non-spam reviews can be identified [27; 37; 38]. Table 10 shows an example of reviewer-centric features. Some common individual spammer features are discussed below:  
**A maximum number of reviews:** Existing works show that mostly spammers write more than one review on a particular day. Hence, this spammer behavioral feature may help to detect spammers [39; 40; 41]. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Reviewer** & **Product** & **Reviewer** & **Product** & **Rating** & **Useful** & **Date the Last** & **Max Number** \\
**Number** & **ID** & **ID** & **I** & **Reviews** & **Review** & **Review Was** & **of Reviews** \\  & & & & & & **Written** & **Written** & **per Day** \\ \hline
**Reviewer 1** & 4456 & 3312 & [PERSON]_ADDRESS] & 44 & 4-6-2013 & 8-9-2014 & 15 \\
**Reviewer 2** & 4456 & 4411 & [PERSON]ADDRESS] & 70 & 7-9-2014 & 6-6-2015 & 23 \\
**Reviewer 3** & 4456 & 2211 & [PERSON]DRESS] & 5 & 5-8-2015 & 8-9-2015 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Reviewers characteristics dataset structure. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Review** & **Review** & **Reviewer** & **Product** & **Rating** & **Useful** & **Number** & **Date the Last** & **Max Number** \\
**ID** & **ID** & **ID** & **Reviews** & **Review** & **Review Was** & **of Reviews** \\  & & & & & **Written** & **Written** & **per Day** \\ \hline
**Reviewer 1** & 192 & 332 & 4455 & 3 & 1 & 7 & 3-5-2013 & 07:22 \\
**Reviewer 2** & 193 & 456 & 4455 & 5 & 1 & 8 & 3-5-2013 & 09:33 \\
**Reviewer 3** & 194 & 287 & 4455 & 5 & 0 & 5 & 4-5-2013 & 22:11 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Review characteristics dataset structure. **Percentage of positive reviews:** It is observed that most spammers write positive and favorable reviews, hence a high percentage of positive reviews about any product or service might be an indication of spam reviews [28]. **Review length:** Existing literature shows that most spammers do not write detailed reviews about a product or service, so this useful reviewer-centric feature may help to identify spammers [35; 42]. **Reviewer deviation:** Mostly spammers' ratings deviate from the average review rating. Generally, spammers give a high rating for product or services [40; 43]. **Maximum content similarity:** Similar text content of reviews about different products by a similar reviewer is found to be a strong indication of a spammer [2; 4]. **Discussion**  
Feature engineering is the base for any spam review detection work. The whole process of spam review detection relies upon the selection of a suitable feature engineering approach because the extracted features from the dataset become the input for the spam review detection method. The accuracy of the spam review detection method is highly dependent upon the extracted features [44]. Table 11 shows that linguistic-based approaches provide the best accuracy, as compared to review content-centric and reviewer behavioral feature approaches [4]. It has been observed from the existing studies that there is no real world labeled dataset available in the domain of spam review detection [4].