## 4 LeSSA--Implementation and Technical Details
### Multi-Model Sentiment Learning Layer (MMSLL)
#### 4.2.1 Wide Coverage Sentiment Lexicon Based Sentiment Learner  
Sentiment lexicon plays a vital role in the review document sentiment classification. A sentiment lexicon is a list of words that are used to show positive or negative sentiments [4]. In the absence of abundant training data, the alternative approach of LS has been largely employed by many researchers  
Figure 1: The architecture of the proposed framework. in the domain of textual sentiment classification. In this approach, sentiment words in review documents are matched against sentiment lexicons, and a sentiment value is assigned to matched sentiment words. The overall sentiment orientation of a review document is then determined by using a formula (as shown in Equation (1)). In detail, the sentiment values of matched sentiment words in the review document are aggregated to determine the sentiment orientation for the review document. The architecture of induced WCSL-SL for the review document classification is shown in Figure 2. The detailed process of inducing WCSL-SL along with linguistic rules and semantic knowledge for review document sentiment classification is given in the subsection below. \[\text{Review Sentiment Score}=\text{ Sum of positive sentiment scores - Sum of negative sentiment scores.} \tag{1}\]  
In the literature, several sentiment lexicons such as SentiWordNet, Wordnet-Affect, Micro-WNOP with different sizes and formats have been constructed to classify review documents into positive and negative classes. However, there is no optimum universal/general sentiment lexicon because the semantic orientation of words is sensitive to a particular domain. Besides, words that are not considered sentiment words in the existing sentiment lexicon can express sentiment in a particular domain. Furthermore, the existing sentiment lexicon cannot handle effectively context-dependent sentiment words. Many of the existing sentiment lexicons are beset by having limited words, which are insufficient to calculate the sentiment score of a domain-specific sentiment word. We present an integrated lexicon-based approach utilizing existing sentiment lexicons with linguistic rules and semantic knowledge. Our purpose is to integrate multiple sentiment lexicons to increase the word coverage of sentiment lexicon for sentiment classification. Figure 2: The architecture of WCSL based sentiment classification. Moreover, we identify and extract context-aware domain-specific sentiment words and determine their sentiment scores by leveraging linguistic rules and semantic knowledge. Here we integrate ten state-of-the-art sentiment lexicons intending to improve the limited word coverage of the individual sentiment lexicon. The summary of existing sentiment lexicons is given in Table 2, where the size and format of the sentiment lexicons are different from each other. Some sentiment lexicons contained numeric scores with different ranges, while other assigned positive, negative and neutral classes to sentiment words. Furthermore, some sentiment words were categorized into a variety of emotions such as joy, happiness and sadness. As these sentiment lexicons have distinct forms, it is important to convert them into a standard form. We standardize the sentiment lexicons to have one of three different values, +1, 0, \(-\)1. While for sentiment lexicon integration, we take the average of the sentiment values of the overlapping words, which produces a huge sentiment lexicon with more sentiment words. The detailed process of the sentiment lexicon standardization is explained below. * AFINN: AFINN is a list of English words with an integer between \(-\)5 and 5. We scaled the sentiment scores from [\(-\)5,\(+\)5] to [\(-\)1,\(+\)1] using minmax scale [62]. * General Inquirer (GI): The sentiment words in GI have been categorized into more than 180 classes. We considered positive, strong, pleasure, virtue, pstv, active, complete and yes as positive categories and assign +1 sentiment value to every sentiment word in the category.