yed to analyst opinions, emotions, evaluations, subjective, objective and appraisal at sentence-level into positive or negative opinion by using NLP (Natural Language Processing). It deals with identifying the intensity of adverbs, adjectives, or AAC (Adverb-Adjective Combination). It hypothesizes a score of +1 is positive and +2 or more is (strongly positive) and more than -1 is (maximally negative) whereas a score of 0.0 denotes that the adverb, adjective or overall sentence is (neutral). Furthermore, it clarifies the major procedures of SA including the computational methods variable scoring algorithm, Sentiment Analysis 2.0.0, and MonkeyLearn Sentiment Analyzer. It discusses two models of sentiment analysis Dobrescu, 2011 which investigates the main concepts of SA. The second model is Appraisal Lexicons which has been utilized to extract appraisal expressions as well as appraisal groups based on Appraisal Theory. After data analysis has been collected accurately, the researchers have conducted that the most significant conclusions represent that intensifying adverbs alone are not sentiment-laden. However, adverbs strengthen the semantic conveyed by adjectives. In addition, the researchers have found that adjectives are stronger than adverbs in sentimental analysis. The terms force and focus are very important in classifying appraisal lexicons.