# An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud
## 2 Methodology and Methods Framework
### Pre-Processing of Text  
People constantly express their views and feelings in a sincere way on various social media platforms. Since the information extracted from such social media platform comments, audits, posts, remarks, and complaints are often unstructured, performing sentiment and emotion analysis for computers is challenging [15]. Subsequently, pre-processing is an essential phase in the data-cleaning process because it significantly influences many subsequent operations. Pre-processing techniques such as stop word removal, tokenization, and POS labeling are required due to the nature of a dataset. Action must be taken since some of these pre-processing methods could cause data loss essential for sentiment and emotion research. Two crucial stages in pre-processing are lemmatization and stemming. Truncating suffixes are used in stemming when restoring words to their original form [16]. For instance, \"argued\" and \"argue\" are changed to \"argue\". With this approach, there is no longer a need for pointless sentence calculations. The lemmatization process consists of removing inflectional ends from a token to transform it into the lemma at its base. For example, the word \"caught\" is changed to \"catch\". On two datasets, SS-Tweet and SemEval, researchers evaluated using combination and ablation analysis on the performance of four machine learning models through several pre-processing strategies. Lemmatization and number deletion had no effect on accuracy, but punctuation deletion did, according to the scientists' findings. To determine the grammatical category to which a word belongs based on context, such as whether it is an adjective, adverb, noun, verb, or another part-of-speech tagging (POS tagging) is used, following a relationship analysis of the phrase, each word is assigned the proper tag [17]. Text normalization attempts to make the text less unpredictable and to bring it closer to a predetermined standard. Giving succeeding algorithms less unique information to work with increases efficiency using two methods, stemming and lemmatization. Such normalization methods seek to reduce a word's inflectional and occasionally derivative forms to their basic form. The process of stemming involves reducing words to their basic form. Using a list of frequently occurring prefixes and suffixes, stemming algorithms work by deleting a word's starting or ending [18]. This haphazard cutting does not always succeed. Thus, this tactic has many drawbacks, and the words could be reduced to their purest form through lemmatization. The lemmatization algorithms correctly reduce inflected words to preserve the linguistic relationship to the underlying word. The stemming algorithm may not be able to distinguish between words with multiple meanings based on the word type since it only understands the context of the individual word rather than the context of the entire phrase. This is the distinction between lemmatization and \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Data Size** & **Sentiments/Emotions** & **Range** \\ \hline Alm gold standard & & Feelings of joy, fear, & \\ data set & & sadness, surprise, and & 5 \\  & & disgust-anger & \\ \hline EmoTex & 52,003 & Excellent, excellent, very & \\  & & negative, very negative, & 5 \\  & & and neutral. & \\ \hline Text Affect & 15,763 & Extremely positive, very & \\  & & positive, negative, & \\  & & extremely negative, & \\  & & and neutral. & \\ \hline [PERSON] Dataset & 20,000 & Very positive, very & \\  & & positive, very negative, & \\  & & and neutral. & \\ \hline [PERSON]’s dataset & 3200 & Neutral, Positive, very & \\  & & positive, negative, and & 7 \\  & & very negative. & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._stemming. Stemming algorithms have the benefit of being simpler to design and apply. Stemming algorithms are the best choice if precision is not required for the application [19].