# An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud
###### Abstract  
Sentiment analysis (SA), and emotion detection and recognition from text (EDRT) are recent areas of study that are closely related to each other. Sentiment analysis strives to identify and detect neutral, positive, or negative feelings from text. On the other hand, emotion analysis seeks to identify and distinguish types of feelings such as happiness, surprise, grief, disgust, fear, and anger through the expression of texts. We suggest a four-level strategy in this paper for recommending the best book to users. The levels include semantic network grouping of comparable sentences, sentiment analysis, reviewer clustering, and recommendation system. The semantic network groups comparable sentences at the first level utilizing pre-processed data from reviewer and book datasets using the parts of speech (POS) tagger. In order to extract keywords from the pre-processed data, feature extraction uses the bag of words (BOW) and term frequency-inverse document frequency (TF-IDF) approaches. SA is performed at the second level in two phases: training and testing, employing deep learning methodologies such as convolutional neural networks (CNN)-long short-term memory (LSTM). The results of this level are sent into the third level (clustering), which uses the clustering method to group the reviewers by age, location, and gender. In the last level, the model assessment is carried out with accuracy, precision, recall, sensitivity, specificity, G-mean, and F1-measure. The book suggestion system is designed to provide the highest level of accuracy within a minimum number of epochs when compared to the state-of-the methods, SVM, CNN, ANN, LSTM, and Bi-directional (BI)-LSTM. s _aciences_  
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are unstructured and written in plain English. Additionally, evaluations can be published in non-grammatically exact local languages and slang. The historical parsers created previously may not be suited for such material. For example, imagine someone wrote on CookingLight.com (Recipe Site) about a recipe for Mexican Pizza and said, \"Though I attempted the same method as stated, the meal tasted excessively salty\". We can use sentiment analysis to see whether the reviewer enjoyed the food. \"Too salty\" can be interpreted as a negative attribute, indicating that the client did not want the food [4]. Sentiment analysis uses novel categorization algorithms to solve these issues. Its purpose might be as primary as a positive or negative (binary classification) or as complex as a multi-class classification. It is often recommended to customize opinion mining to extract subject-specific evaluations because reviews contain people's thoughts on several issues. Sentiment analysis can also be conducted on a broader or more detailed level [5]. The method of identifying whether any piece of literature, an abstract, or a sentence is neutral, positive, or negative is often categorized as a sentiment analysis. It is also referred to as opinion mining since it derives a speaker's or writer's viewpoint on a topic or the general context of a document [6]. For online reviews or social media, sentiment analysis is employed to ascertain how customers or people feel about a problem. Finding the text's polarity in a sentence or document, or whether their expressed attitude is neutral, positive, or negative, is one of the main objectives of sentiment analysis. Figure 1 shows the stages involved in a sample sentiment analysis in a product review. People today produce a massive volume of information through casual writing in the Internet age. Most social networking platforms offer challenges such as improper grammar, spelling mistakes, and strange vocabulary. These difficulties make sentiment and emotion analysis challenging for robots [7]. People do not always clearly express their emotions. For instance, when asking \"Why have you been so late?\", \"why\" might be spelled incorrectly as \"y\", the word \"you\" might also be misspelled as \"u\", and \"sooooo\" might be use to emphasize a point.