Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM

User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings

Using Multi-Label Multi-Class Support Vector Machines with Semantic and Lexical Features for Aspect Category Detection

In contrast to the aspects, aspect categories are often coarser and don't always appear as terms in sentences. Besides, the typical way to element the types associated with part is generally grainier concerning factors and doesn't exist within verdicts. The primary intent of the study is to investigate the efficacy of Lexicon, linguistic, vector-based, and features correlated to semantics within the aspect of the responsibility built with the finding of aspect category detection ACD). Semantic and emotional data are captured via vector-based features. Further, it examines vector-based feature superiority issues within the compression of features of text-based characteristics. Study purposes to the linguistic efficacy with the Lexicon, linguistic, and semantic features, also vector-based dependent to the system. Also, the information led with vector-based features that capture the semantic with sentimental analysis characteristics. With the experimental outcomes, the performance efficacy with the vector-based features outperformed text-based features. The methodologies associated with deep learning have generated features within the vector orientation relevant to the word-based structures. Therefore, the proposed method achieved effectiveness with the determined constraints by applying the metrics of precision, recall, and F1 scores. Correlating with the performance of ABSA's state-of-the-art techniques, the proposed research process gained superior outcomes

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews

These days with TV-shows and starred chefs, new kinds of cuisines appear in the market. The main cuisines like French, Italian, Japanese, Chinese and Indian are always appreciated but they are no longer the most popular. The new trend is the fusion cuisine, which is obtained by combining different main cuisines. The opening of a new restaurant proposing new kinds of cuisine produces a lot of excitement in people. They feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd sourced reviews about different businesses, in particular, restaurants. For some restaurants in Yelp if the kind of cuisine is available, usually, there is a tag only for the main cuisines, but there is no information for the fusion cuisine. There is a need to develop a system which is able to identify restaurants proposing fusion cuisine (novel or unknown cuisines). This proposal is to address the novelty detection task using Yelp reviews. The idea is that the semi-supervised Machine Learning models trained only on the reviews of restaurants proposing the main cuisine will be able to discriminate between restaurants providing the main cuisine and restaurants providing the novel ones. We propose effective novelty detection approaches for the unknown cuisine type identification problem using Long Short Term Memory (LSTM), autoencoder and Term-Frequency and Inverse Document Frequency(). Our main idea is to obtain features from LSTM, autoencoder and TF-IDF and use these features with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest and One-class Support Vector Machines (SVM) to identify the unknown cuisines. We conducted extensive experiments that prove the effectiveness of our approaches. The score that we obtained has a very high discrimination power because the best value of AUROC for the novelty detection problem is 0.85 from LSTM. LSTM outperforms our baseline model of TF-IDF and the main motivation is due to its ability to retain only the useful parts of a sentence

Deep Learning for Learning Representation and Its Application to Natural Language Processing

As the web evolves even faster than expected, the exponential growth of data becomes overwhelming. Textual data is being generated at an ever-increasing pace via emails, documents on the web, tweets, online user reviews, blogs, and so on. As the amount of unstructured text data grows, so does the need for intelligently processing and understanding it. The focus of this dissertation is on developing learning models that automatically induce representations of human language to solve higher level language tasks. In contrast to most conventional learning techniques, which employ certain shallow-structured learning architectures, deep learning is a newly developed machine learning technique which uses supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures and has been employed in varied tasks such as classification or regression. Deep learning was inspired by biological observations on human brain mechanisms for processing natural signals and has attracted the tremendous attention of both academia and industry in recent years due to its state-of-the-art performance in many research domains such as computer vision, speech recognition, and natural language processing. This dissertation focuses on how to represent the unstructured text data and how to model it with deep learning models in different natural language processing viii applications such as sequence tagging, sentiment analysis, semantic similarity and etc. Specifically, my dissertation addresses the following research topics: In Chapter 3, we examine one of the fundamental problems in NLP, text classification, by leveraging contextual information [MLX18a]; In Chapter 4, we propose a unified framework for generating an informative map from review corpus [MLX18b]; Chapter 5 discusses the tagging address queries in map search [Mok18]. This research was performed in collaboration with Microsoft; and In Chapter 6, we discuss an ongoing research work in the neural language sentence matching problem. We are working on extending this work to a recommendation system

Machine learning-based recognition on Crowdsourced Food Images

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further. Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in the field of health geography, we aim to utilize image recognition to gather and model the role of the community food environment in shaping obesity and related chronic diseases

Extraction of Visual Information to Predict Crowdfunding Success

Researchers have increasingly turned to crowdfunding platforms to gain insights into entrepreneurial activity and dynamics. While previous studies have explored various factors influencing crowdfunding success, such as technology, communication, and marketing strategies, the role of visual elements that can be automatically extracted from images has received less attention. This is surprising, considering that crowdfunding platforms emphasize the importance of attention-grabbing and high-resolution images, and previous research has shown that image characteristics can significantly impact product evaluations. Indeed, a comprehensive review of empirical articles (n = 202) that utilized Kickstarter data, focusing on the incorporation of visual information in their analyses. Our findings reveal that only 29.70% controlled for the number of images, and less than 12% considered any image details. In this manuscript, we review the literature on image processing and its relevance to the business domain, highlighting two types of visual variables: visual counts (number of pictures and number of videos) and image details. Building upon previous work that discussed the role of color, composition and figure-ground relationships, we introduce visual scene elements that have not yet been explored in crowdfunding, including the number of faces, the number of concepts depicted, and the ease of identifying those concepts. To demonstrate the predictive value of visual counts and image details, we analyze Kickstarter data. Our results highlight that visual count features are two of the top three predictors of success. Our results also show that simple image detail features such as color matter a lot, and our proposed measures of visual scene elements can also be useful. We supplement our article with R and Python codes that help authors extract image details (https://osf.io/ujnzp/).Comment: 32 pages, 5 figure

Weakly supervised sentiment analysis and opinion extraction

In recent years, online reviews have become the foremost medium for users to express their satisfaction, or lack thereof, about products and services. The proliferation of user-generated reviews, combined with the rapid growth of e-commerce, results in vast amounts of opinionated text becoming available to consumers, manufacturers, and researchers alike. This has fuelled an increased focus on automated methods that attempt to discover, analyze, and distill opinions found in text. This thesis tackles the tasks of fine-grained sentiment analysis and aspect extraction, and presents a unified framework for the summarization of opinions from multiple user reviews. Two core concepts form the basis of our methodology. Firstly, the use of neural networks, whose ability to learn continuous feature representations from data, without recourse to preprocessing tools or linguistic annotations, has advanced the state-of-the-art of numerous Natural Language Processing tasks. Secondly, our belief that opinion mining systems applied to real-life applications cannot rely on expensive human annotations and should mostly take advantage of freely available review data. Specifically, the main contributions of this thesis are: (i) The creation of OPOSUM, a new Opinion Summarization corpus which contains over one million reviews from multiple domains. To test our methods, we annotated a subset of the data with fine-grained sentiment and aspect labels, as well as extractive gold-standard opinion summaries. (ii) The development of two weakly-supervised hierarchical neural models for the detection and extraction of sentiment-heavy expressions in reviews. Our first model composes segment representations hierarchically and uses an attention mechanism to differentiate between opinions and neutral statements. Our second model is based on Multiple Instance Learning (MIL), and can detect user opinions of potentially opposing polarity. Experiments demonstrate significant benefits from our MIL-based architecture. (iii) The introduction of a neural model for aspect extraction, which requires minimal human involvement. Our proposed formulation uses aspect keywords to help the model target specific aspects, and a multi-tasking objective to further improve its accuracy. (iv) A unified summarization framework which combines our sentiment and aspect detection methods, while taking redundancy into account to produce useful opinion summaries from multiple reviews. Automatic evaluation, on our opinion summarization dataset, shows significant improvements over other summarization systems in terms of extraction accuracy and similarity to reference summaries. A large-scale judgement elicitation study indicates that our summaries are also preferred by human judges

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models