# JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation
## 2 Literature Survey  
In the past couple of years, researchers have worked on various recommendation systems based on text data provided by netizens. [PERSON] et al. [1] proposed a study based on the \"Sentiment Polarity Dataset version 2.0\" from the IMDB website ([PERSON] and [PERSON] [2]), which is a two-class dataset. The classifiers used were Naive Bayes (NB), K-Nearest Neighbour (KNN), and RF. The word embedding was provided by the StringToWordVector filter. [PERSON] et al. [3] presented a political SA model that used twitter data. They utilised Term Frequency-Inverse Document Frequency (TF-IDF), which is a term weight-based embedding system, combined with simple Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. [PERSON] et al. [4] as well as [PERSON] used document frequency-based vocabulary but implemented feature selection based on information gain. SVM and Neural Network (NN)-based models were used to classify reviews in the the same dataset as [PERSON] et al. [1]. [PERSON] et al. [5] and [PERSON] worked on the SA of IMDB movie review data using unigram, bigram, and uni+bigram models. They used NB, Maximum Entropy, and SVM classifiers to achieve a two-class classification. [PERSON] et al. [6] and [PERSON] et al. [7] furthered the work of [PERSON] et al. [5] by adding TF-IDF, POS tagging, and Stochastic Gradient Descent (SGD) in order to improve the accuracies. [PERSON] et al. [8] developed the work of [PERSON] et al. [6] and used the chi-squared method of feature selection along with the already proposed SVM classifier. [PERSON] et al. [9] proposed a hotel recommendation system using a sentiment analysis of the hotel reviews and an aspect-based review categorisation that works on the queriesgiven by a user. They provided a new rich and diverse dataset of online hotel reviews crawled from Tripadvisor.com. They followed a systematic approach, which first used an ensemble of a binary classification called the Bidirectional Encoder Representations from Transformers (BERT) model, with three phases for positive-negative, neutral-negative, and neutral-positive sentiments merged using a weight assigning protocol. The authors also grouped the reviews into different categories using an approach that involved fuzzy logic and cosine similarity. Finally, they created a recommender system with the aforementioned frameworks. Our model achieved a Macro F\({}_{1}\)-score of 84% and test accuracy of 92.36% in the classification of sentiment polarities. [PERSON] and [PERSON] [10] put forward a product review-based recommendation system from reviews on Amazon.com. Their categorisation was both review-based and sentence-based. Part-of-speech (POS) tagging and sentiment-phrase identification were performed to calculate a sentiment score. These were considered, along with the rating system, which allowed users to rate the product from one-half to five stars. This two-level scoring system was combined with NB, RF, and SVM classifiers. [PERSON] and [PERSON] [11] provided an embedding method based on collaborative filtering using the word2 vec skip-gram negative sampling method (SGNS) named 'item2 vec'. This was used on a music dataset with artists pertaining to several different genres. When compared to a singular value decomposition classified with a simple KNN model, the embedding resulted in a more accurate score. [PERSON] et al. [12] used opinion mining based on the Gini index by extracting opinion-oriented words (top 50 words). When performed on several web and self-crawled databases, the result was fed to an SVM classifier. This method does not account for sentences that may contain unopinionated words and yet carry opinions, nor does it deal with sentences that might contain strong polarising words and yet not contain important opinions. Both these works were based on two-class classification. [PERSON] et al. [13] propounded the use of a deep learning-based classifier along with MR and STS-Gold as the benchmark datasets ([PERSON] et al. [14]) to give a two-class classification.