sentiment analysis IS AN AREA OF STUDY WITHIN NATURAL LANGUAGE PROCESSING THAT IS CONCERNED WITH IDENTIFYING THE MOOD OR OPINION OF SUBJECTIVE ELEMENTS WITHIN A TEXT. THIS PAPER FOCUSES ON THE VARIOUS METHODS USED FOR CLASSIFYING A GIVEN PIECE OF NATURAL LANGUAGE TEXT ACCORDING TO THE OPINIONS EXPRESSED IN IT I.E. WHETHER THE GENERAL ATTITUDE IS NEGATIVE OR POSITIVE. WE ALSO DISCUSS THE TWO-STEP METHOD (ASPECT CLASSIFICATION FOLLOWED BY POLARITY CLASSIFICATION) THAT WE FOLLOWED ALONG WITH THE EXPERIMENTAL SETUP.

SOCIAL NETWORKS ARE THE MAIN SOURCE OF USER OPINIONS ABOUT EVENT AND PRODUCT. EXTRACTING USER sentiment FROM THEIR COMMENTS IN SOCIAL NETWORKS VERY HELPFUL FOR COMPANIES AND GOVERNMENTS FOR THEIR DEVELOPMENT PLAN. TWITTER CONSISTS OF BILLIONS OF USER AND THEIR OPINIONS AND IT IS A GOOD SOURCE FOR sentiment analysis. LOTS OF WORKS PROPOSED IN RECENT YEARS ABOUT sentiment analysis IN TWITTER. VARIOUS METHODS ARE USED TO DEVELOP A SA METHOD SUCH AS NLP BASED, MACHINE LEARNING BASED AND HYBRID METHODS. BUT, ALL OF THESE METHODS DON’T SATISFY ALL REQUIREMENTS OF THIS RESEARCH AREA. IN THIS REPORT WE TRY TO REVIEW THE IMPORTANT SOLUTIONS ARE PROPOSED FOR THIS PROBLEM.THIS PAPER CONSISTS OF FIVE CATEGORIES: 1) TO INTRODUCE AND TO ORIENTATE WITH THE FIELD OF sentiment analysis IN TWITTER SOCIAL NETWORKS 2) TO REVIEW THE WORKS DONE IN THE AREA OF SA 3) TO INTRODUCE CONFERENCES RELATED TO THE FIELD OF SA IN RECENT YEARS THAT HOLD COMPETITIONS WITH THEIR RESULTS AND THEIR BEST PRACTICES OFFERED 4) TO INTRODUCE AVAILABLE DATASETS 5) TO INTRODUCE A FEW AVAILABLE MASH-UPS.

Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-depth analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian sentiment analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253, 368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper.

Recently, interests in the appliance of deep learning techniques in natural language processing tasks considerably increased. sentiment analysis is one of the most difficult tasks in natural language processing, mostly in the Persian Language. Thousands of websites, blogs, social networks like Telegram, Instagram and Twitter update, and modify by Persian users around the world that contains millions of contexts. To extract knowledge of these huge amounts of raw data, Deep Learning techniques became increasingly popular but there is a number of challenges that the novel models encounter with them. In this research, a hybrid deep learning-based sentiment analysis model proposed and implemented on customer reviews data of Digikala Online Retailer website. We already applied the classifier based on various deep learning networks and regularization techniques. Finally, by utilizing a hybrid approach, we achieved the best performance of 78. 3 of F1 score on three different classes: positive, negative, and neutral.