Analysis of Twitter Data Using Deep Learning Approach: LSTM

Sentiment analysis the procedure of computationally identifying and categorizing evaluations expressed in a chunk of text, especially with a view to decide whether the writer’s mind-set toward a selected subject matter, product, etc. is high-quality, poor, or impartial[1]. Now a days the growth of social websites, running a blog offerings and electronic media con-tributes big amount of consumer supply messages which includes customer reviews, remarks and evaluations. Sentiment evaluation is an important term cited gather facts in a source with the aid of the usage of NLP, computational[2] linguistics and text analysis and to make decision through subjective information extracting and analyzing opinion, figuring out advantageous and bad opinions measuring how definitely and negatively an entity (public ,organization, product) is concerned. in the beyond decade , researcher have performed the sentiment analysis using device getting to know techniques which include guide vector gadget, naive bayes , maximum entropy method etc. Sentient analysis on social media textual content received lot of recognition because it includes pointers and pointers. lately deep gaining knowledge of methods like long short-term memory (LSTM) and convolution neural network (CNN) have gained recognition by means of displaying promising effects for speech and photograph processing, obligations in NLP through learning functions wealthy deep illustration from the facts robotically

Analisis Sentimen Temporal Tentang Kuliner Di Kota Surabaya Berbasis Gender Menggunakan Bahasa Indonesia

Surabaya is the capital city of East Java Province, Indonesia, and the largest metropolitan city in the province. Surabaya is the largest city in Indonesia after Jakarta. With its large area and a fairly dense population, the city of Surabaya has a variety of culinary to offer. This culinary diversity attracts responses and opinions from the public (both positive and negative). People have certain ways to express their opinions, including through social media. Opinions shared by the Indonesians about Surabaya’s culinary in social media provide the right step to know what people think about the culinary places in Surabaya. But these opinions are very diverse because social media does not decide what people will say. We need opinion analysis to gain uses information from those opinions. In this research, we offered a new approach in analyzing the opinion on the culinary places in Surabaya using temporal analysis based opinion mining. The source is the opinions expressed in Foursquare using Indonesian. The rate of the accuracy is still around 67.32%, because other than Indonesian as the formal language used in the express opinions, social media users there also use many local dialects and slangs.Kota Surabaya adalah ibu kota Provinsi Jawa Timur, Indonesia sekaligus menjadi kota metropolitan terbesar di provinsi tersebut. Surabaya merupakan kota terbesar kedua di Indonesia setelah Jakarta. Dengan luas yang sangat besar dan penduduk yang cukup padat bisa dipastikan bahwa kota Surabaya memiliki beragam kuliner. Pastinya keberagaman kuliner ini memberi banyak respon dan opini dari masyarakat baik positif maupun negatif. Banyak cara yang digunakan masyarakat dalam mengutarakan opini mereka, salah satunya yaitu melalui sosial media. Opini-opini yang disampaikan masyarakat Indonesia tentang kuliner di Surabaya dari sosial media merupakan langkah yang tepat untuk mengetahui pemikiran masyarakat akan kuliner di Surabaya. Tetapi opini-opini tersebut sangat beragam karena media sosial tidak pernah membatasai pemikiran seseorang. Analisis opini adalah cara yang tepat untuk mencari informasi dari opini-opini tersebut. Pada penelitian ini, diajukan sebuah pendekatan baru dalam analisis opini tentang kuliner di Surabaya yaitu dengan opinion mining berbasis temporal sentiment analysis. Bahasa yang digunakan adalah Bahasa Indonesia dengan media social Foursquare sebagai sumber opininya. Adapun hasil akurasi yang didapatkan, masih berada di sekitar 67,32%, dikarenakan di dalam Bahasa Indonesia, selain bahasa baku yang digunakan dalam menyampaikan opini, terdapat juga banyak ragam bahasa daerah dan bahasa gaul yang digunakan oleh pengguna media sosialnya

Opinion Mining and Sentiment Analysis using Bayesian and Neural Networks Approaches

Infotehnoloogiad on muutunud suureks osaks meie elust ja praeguseks on raske kujutada ette elu ilma vidinate ja internetita. Sotsiaalmeedia ei ole tänapäeval ainult informatsiooniallikas, vaid lubab kasutajatel ka omavahel suhelda ning jagada üksteisega arvamusi ja kogemusi. Teatud osa sellest infost on subjektiivne ning sisaldab kasutaja seisukohtadega seostuvat informatsiooni. Säärast informatsiooni analüüsides saab sellest eraldada kõige olulisema ning hiljem kasutada saadud informatsiooni analüüsimiseks ja otsuste tegemistes. Esmalt, et informatsiooni sellisel kujul kasutada, on vaja seda mõista ja kategoriseerida. Käesolevas töös õpitakse seisukohtade analüüsimise tehnikaid, et siis säutsudest arvamusi eraldada. Efektiivseks klassifitseerimiseks on oluline rakendada ülesande lahendamiseks algoritme, mis saavad sellega edukalt hakkama. Magistritöö põhieesmärgiks on uurida algoritme, mida saaks kasutada seisukohtade hindamiseks. Teostatakse andmete eeltöötlust ja viiakse läbi mitmeid eksperimente. Klassifitseerijat treenitakse ja testitakse kahe erineva andmekogu peal kasutades kahte erinevat klassifitseerija implementatsiooni, milleks on naiivne Bayes ja konvolutsiooniline närvivõrk. Lisaks arutatakse klassifitseerija efektiivsuse üle ja mis mõju avaldavad sellele andmed, mille peal seda treenitakse.Information technologies have firmly entered our life and it is impossible to imagine our life without gadgets or the Internet. Today, social media is not only a source that broadcasts information to the users, but it allows users to intercommunicate and share their views and experience with each other. Some portion of such data is subjective and contains opinionated information that can be further analyzed to retrieve essential data from it and later use for various purposes for analysis and decision support. In order to use this type of that the first step is to understand it and categorize opinions in the information. Hence, in this dissertation, sentiment analysis techniques are studied in order to retrieve opinions from the tweets. In order to ensure efficient classification, it is important to apply algorithms that perform well on this task. Therefore, the main goal of the thesis is to investigate algorithms that can be applied for the opinion estimation. To that extend, data preprocessing and several experiments are conducted, namely, the classifier is trained and tested on two different datasets with two different classifiers (Naive Bayes and convolutional neural network). In addition, the influence of the training data on the classifier efficiency is discussed

Penggalian Opini Pada Ulasan Buku Menggunakan Algoritma CNN - L2-SVM

Ulasan suatu produk dapat merepresentasikan kualitas dari produk tersebut. Suatu ekstraksi terhadap ulasan tersebut dapat digunakan untuk mengetahui sentimen dari opini yang diutarakan. Proses untuk mengekstraki informasi yang berguna dari ulasan pengguna disebut Opinion mining. Model ekstraksi ulasan yang berkembang sekarang yaitu model Deep Learning. Model tersebut telah banyak digunakan untuk mendapatkan pencapaian performansi pada Natural Language Processing. Pada Tugas Akhir ini digunakan salah satu metode deep learning yaituConvolutional Neural Network (CNN) sebagai ekstraksi fitur ulasan dan dilakukan klasifikasi dengan menggunakan L2 Support Vector Machine (SVM). Metode diimplementasikan untuk dapat mengetahui sentimen dari data ulasan buku. Hasil dari metode tersebut menunjukkan performansi pembelajaran sekitar 83.23% dan performansi pengujian sekitar 64.6 %. ================================================================= Review of a product can represent quality of a product itself. An extraction to that review can be used to know sentiment of that opinion. Process to extract usefull information of user review is called Opinion Mining. Review extraction model that is enhacing nowadays is Deep Learning model. This Model has been used by many researchers to obtain excelent performation on Natural Language Processing. In this final project, one of deep learning model, Convolutional Neural Network (CNN) is used for feature extraction and L2 Support Vector Machine (SVM) as classifier. These methods are implemented to know the sentiment of book review data. The result of this method shows state-of-the art performance in 83.23% for training phase and 64.6% for testing phase

Automatic seed word selection for unsupervised sentiment classification of Chinese text

We describe and evaluate a new method of automatic seed word selection for unsupervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not require any annotated training data; it only requires information about commonly occurring negations and adverbials. Unsupervised techniques are promising for this task since they avoid problems of domain-dependency typically associated with supervised methods. The results obtained are close to those of supervised classifiers and sometimes better, up to an F1 of 92%

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data