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Restaurant Review Classification and Analysis Vol 11, Issue 8, August/ 2020 ISSN NO: 0377-9254 Restaurant Review Classification and Analysis Dhiraj Kumar1, Gopesh2, Avinash Choubey3, Ms.Pratibha Singh4 1Student, Computer Science & Engineering Department ABES Engineering College Ghaziabad, U.P. 2Student, Computer Science & Engineering Department ABES Engineering College Ghaziabad, U.P. 3Student, Computer Science & Engineering Department ABES Engineering College Ghaziabad, U.P. 4Faculty, Computer Science & Engineering Department ABES Engineering College Ghaziabad, U.P. Abstract- Restaurants nowadays prefer taking online orders. It not only helps in getting effective customer feedback but 1.Problem Introduction also useful for managing orders easily. We are moving towards an automated and For years food and hospitality businesses are digital world. Having a significant online running on the assumption that good food and presence is necessary for any restaurant to service is the way to attract more customers. be successful and prosperous. Getting But the advent of science and technology, customer feedback and analyzing them in more importantly, the data created by the use an effective manner makes the difference. of online platforms has pointed towards new This study analyses the restaurant reviews findings and opened new doors: Most and presents useful information that the consumers nowadays rate a product online, ratings do not consider or overlook. over 1/3rd of them write reviews and nearly Combined research is done using two 88% of the people trust online reviews. different datasets of restaurant reviews in Review Services like Yelp, Google Reviews, this paper. Machine learning algorithms etc. provide customers and businesses a way like Naïve Bayes and Logistic regression is to interact with one another. Reviews and used for first classifying the reviews in Ratings are useful sources of information but proper aspects then performing sentiment significant problems exist in extracting analysis on them. Summarization is done relevant information and predicting the using gensim and results are displayed future through analysis and correlation of the using effective visualization techniques. existing data. Each day thousands of Future work is also discussed so that an restaurants and businesses are reviewed by efficient analysis system can be developed the customers. utilizing the potential of reviews. The main objective of the work proposed in Keywords- Support Vector Machine (SVM), this paper is to enhance the user experience Naive Bayes classifier, Sentiment Analysis, by analyzing the reviews of restaurants and Topic Modelling, aspect classification. categorize them in some aspects so that a user can easily know about the restaurant. Restaurants are not able to utilize reviews for their businesses. We want to use the aspects that are important in the food and service industry so that we can analyze the sentiment of text reviews and help them to improve their businesses. This research paperwork www.jespublication.com . Page No:169 Vol 11, Issue 8, August/ 2020 ISSN NO: 0377-9254 can be applied to many other industries gives highly accurate results for the related to food and hospitality. theoretical types of challenges. The phrases and expressions of n-gram [11] give it an 2. Related Previous Work edge over all other techniques used for a technical set of challenges. The results When diving into this research work, we explained the effectiveness of sentiment found that a large amount of relevant work analysis challenges for improving the has already been done in this field but what accuracy of the model [12]. was missing was the fact that most of them were not industry oriented. We tried to 2.2. Aspect based Sentiment Oriented incorporate the most noticeable findings in Summarization of Hotel Reviews these works as our base so we could build upon the work already done. Most of the work focuses on improving the models for Due to the unstable size of review dimensions classification. and customer produced content, different text analytic approaches like opinion mining [13], This research paper will help the sentiment analysis, topic modeling [14], aspect classification, play a significant role in researchers to learn and help them to take analyzing the content. Topic Modelling can further implementations and improvements. find diverse topics in a corpus of text because New methods like semantic orientation have of its statistical nature. For every aspect type, also been discussed. The Fakeness of the there is a certain opinion linked to it and the reviews is a common problem that arises. Sentiment analysis method can effectively Some deep learning techniques have also bring out these emotions. been compared with classical techniques. Whether it is a business intelligence problem or a case of unstructured document Some of these works have been summarized categorization sentiment analysis is useful in the subsequent sections. for most of the cases. It has emerged as the most important aspect of the Information 2.1. A survey of Sentiment Analysis Retrieval process. The strategies regarding Challenges text summarization [15] can boost sentiment analysis research. The opinion mining of the hotel reviews is done using SentiWord[16] The consequences of challenges in the area of library. The reviews were summarized on sentiment analysis [1] has been discussed. different aspects and sentiment analysis was Sentiment review structure is compared with performed. [17] sentiment analysis challenges in the first distinction. The effect of this distinction shows that domain-dependence [2] is an 2.3. Assessing the Helpfulness of Online important part of sentiment challenges. The Hotel Reviews second comparison deals with the accuracy of sentiment analysis models based on the challenges. Structured [3], Semi-structured A review can be considered useful for [4], and Unstructured [5] are three types of decision making purposes only when it is review structures that were used for the first thoughtful and insightful. The indicators comparison. Theoretical and technological representing the importance of reviews are are the two types of sentiment analysis different for diverse research areas due to challenges. The challenges include Domain ease of access. In the case of travel and dependence, negation [6], bipolar words [7], hospitality websites, the reviews containing entity feature/ keyword extraction [8], spam, maximum votes are considered to be more or fake review[9], NLP overheads like (short informative and useful for consumers. It can abbreviations, ambiguity, emotions, be helpful in optimizing the cost of the search sarcasm). Parts-of-speech (POS) tagging [10] www.jespublication.com . Page No:170 Vol 11, Issue 8, August/ 2020 ISSN NO: 0377-9254 for most of the consumers by using feature crafted to train the SVM model. Its engineering.[18]. effectiveness in the classification of binary targets is also an important trait to support 2.4. A framework for Fake Review this performance. After analysis of different datasets, it is observed that SVM performed Detection in Online better in almost all of the aspect analysis problems [23] consisting of small datasets. This paper paves an idea of the challenges that we could face in our research. Specifying Generally, aspect-based sentiment analysis is the significance of online feedback for divided into 4 main parts: different types of industries and the amount of difficulty attached in procuring and 1. Extraction of aspect-terms maintaining a favorable honor on the 2. Recognition of polarity aspect-terms Internet, diverse methods have been used to 3. Recognition of aspect-category enhance digital existence, including 4. Recognition of polarity of aspect- unethical practices. Fake reviews are one of category [22] the most preferred unethical methods which exist on sites such as Yelp or TripAdvisor. In response to that Fake Feature Framework 3. FOLLOWING TWO DATASETS ARE (F3), helps to assemble and constitute USED FOR REVIEW ANALYSIS features for fake reviews choice. F3 estimates knowledge obtained from both the user 3.1 SemEval-2014 Task-4 Dataset (personal profile, reviewing activity, trusting information, and social interactions) and review elements (review text).[19] For aspect classification, the dataset is taken from SemEval-2014 Task-4. Our dataset consists of over 3 thousand English sentences 2.5. Sentiment Analysis of Hotel Reviews from different restaurant reviews. The dataset format is in XML consisting of reviews with It is observed that Semantic orientation can their aspects and polarity. Terms also also be used as a sentiment analysis model to mentioned in the dataset on which the aspects classify reviews as 0 or 1 representing are based. There is more than one aspect negative and positive respectively. It is related to some of the reviews [24]. possible to classify a review on the foundation of the average semantic 3.2 Zomato Bangalore Restaurant Dataset orientation of phases in the review which comprises adverbs and adjectives. It is Zomato Bangalore restaurant dataset expected that there will be an efficient value uploaded in 2019. The data was scraped from when we merge semantic orientation [20] Zomato for educational purposes only. with sentiments. The review is recommended Dataset consists of 17 columns with 51717 only if the mean is positive and otherwise not unique URLs and 8792 Unique restaurants. Some of the fields in location and phone are it is recommended. The Naive Bayes model missing. We ignore the column Phone no. generally performs better than SVM [21]. The review list columns contain reviews of restaurants for a specific restaurant. The 2.6. Deep Recurrent Neural Network Vs. Support Vector In this paper, it was discussed that SVM which is one of the supervised learning techniques performs better than other approaches and models. It is because of the fact that the features are carefully hand- www.jespublication.com . Page No:171 Vol 11, Issue 8, August/ 2020 ISSN NO: 0377-9254 analysis of the restaurants and sentiment preprocessed data obtained from phase 2. classification is done using this dataset [25]. The sentiments are predicted for each aspect Fig. 1. Flow diagram of the proposed method 4.
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