## 2 Proposed Approach
### Data Analysis  
In this section, requirements are analyzed to determine VR and CR. The SPL engineers assure that requirements must be quantifiable and detailed. The success of any SPL depends on the correct identification of domain requirements in the RE phase [30]. The attributes of tweets and comments such as number of likes, number of replies (discussion) for a comment or a tweet and frequency (duplication) were selected to determine the VR and CR. These factors are called influence factors in our study. The numbers of likes, replies, and frequency or duplication are selected as influence factors to determine the VR and CR. A higher influenced tweet or  
Figure 3: Example of restoration and transformation of collected opinions. comment can tell that the majority of people are taking interest in it. Therefore, we propose following Equation (2) to determine the VR and CR of requirements. \[I_{op}=\ \left(W_{L\&bs}\ *\ N_{L\&bs}\right)\ +\left(W_{Duplicates}*N_{Duplicates}\ \ \right)+\left(W_{Replies}*\ N_{Replies}\right) \tag{1}\]  
\[I_{op}=\sum_{k=1}^{3}W_{k}*N_{k}\ (op) \tag{2}\]  
where, \(W_{k}\) is the weighted value for factor of influence, i.e., \(W_{1}\) is the weight for Likes, \(W_{2}\) is the weight for Duplication or similarity and \(W_{3}\) is the weight for Replies. The \(N_{k}\) (\(op\)) represents the numbers of Likes, Duplicates and Replies that a tweet or a comment receives in discussion. The values for Twitter attributes (such as like, retweet, followers and friends, content category, duplicates and sentiment) are determined by a comprehensive survey in Reference [22]. The authors have examined the aforementioned six tweet attributes and rated their importance by a comprehensive survey. In the authors' survey, 84 participants were involved and 76% of the total participants were software developers, 15% were project managers, 4% were product owners and 15% were software engineers. Of the total participants, 38% reportedly had 6-10 years of experience, 30% had 3-5 years of experience, 12% had 1-2 years of experience and 10% had 11-15 years of experience. Furthermore, 66% of the total participants were from industry, 19% were from institutions, 14% were from both industry and research and 1% performed it as a leisure activity. The authors reported that 39% of the total participants were considering tweet attributes while prioritizing the tweets, 44% of the total participants were reportedly sometimes considering tweet attributes and only 17% did not consider tweet attributes while analyzing tweets. This result demonstrates that software engineering researchers and practitioners consider tweet attributes significantly while analyzing tweets. Therefore, based on Reference [31], the weight for each factor in \(W_{k}\) is determined by consulting with four our domain experts. The domain experts argued that the attribute Like is very important to determine the importance of a tweet or a comment because the Like hit shows users' interest in the expressed topic. Therefore, the weight for Likes is agreed to be 0.5. The domain experts also argued that Replies attribute of a tweet or a comment can also be important because it tells about