Sentiment Classification Using N-Gram IDF and Automated Machine Learning
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A Sentiment-Based Chat Bot
A sentiment-based chat bot Automatic Twitter replies with Python ALEXANDER BLOM SOFIE THORSEN Bachelor’s essay at CSC Supervisor: Anna Hjalmarsson Examiner: Mårten Björkman E-mail: [email protected], [email protected] Abstract Natural language processing is a field in computer science which involves making computers derive meaning from human language and input as a way of interacting with the real world. Broadly speaking, sentiment analysis is the act of determining the attitude of an author or speaker, with respect to a certain topic or the overall context and is an application of the natural language processing field. This essay discusses the implementation of a Twitter chat bot that uses natural language processing and sentiment analysis to construct a be- lievable reply. This is done in the Python programming language, using a statistical method called Naive Bayes classifying supplied by the NLTK Python package. The essay concludes that applying natural language processing and sentiment analysis in this isolated fashion was simple, but achieving more complex tasks greatly increases the difficulty. Referat Natural language processing är ett fält inom datavetenskap som innefat- tar att få datorer att förstå mänskligt språk och indata för att på så sätt kunna interagera med den riktiga världen. Sentiment analysis är, generellt sagt, akten av att försöka bestämma känslan hos en författare eller talare med avseende på ett specifikt ämne eller sammanhang och är en applicering av fältet natural language processing. Den här rapporten diskuterar implementeringen av en Twitter-chatbot som använder just natural language processing och sentiment analysis för att kunna svara på tweets genom att använda känslan i tweetet. -
Evaluation of Stop Word Lists in Chinese Language
Evaluation of Stop Word Lists in Chinese Language Feng Zou, Fu Lee Wang, Xiaotie Deng, Song Han Department of Computer Science, City University of Hong Kong Kowloon Tong, Hong Kong [email protected] {flwang, csdeng, shan00}@cityu.edu.hk Abstract In modern information retrieval systems, effective indexing can be achieved by removal of stop words. Till now many stop word lists have been developed for English language. However, no standard stop word list has been constructed for Chinese language yet. With the fast development of information retrieval in Chinese language, exploring the evaluation of Chinese stop word lists becomes critical. In this paper, to save the time and release the burden of manual comparison, we propose a novel stop word list evaluation method with a mutual information-based Chinese segmentation methodology. Experiments have been conducted on training texts taken from a recent international Chinese segmentation competition. Results show that effective stop word lists can improve the accuracy of Chinese segmentation significantly. In order to produce a stop word list which is widely 1. Introduction accepted as a standard, it is extremely important to In information retrieval, a document is traditionally compare the performance of different stop word list. On indexed by frequency of words in the documents (Ricardo the other hand, it is also essential to investigate how a stop & Berthier, 1999; Rijsbergen, 1975; Salton & Buckley, word list can affect the completion of related tasks in 1988). Statistical analysis through documents showed that language processing. With the fast growth of online some words have quite low frequency, while some others Chinese documents, the evaluation of these stop word lists act just the opposite (Zipf, 1932). -
Sentiment Analysis on Interactive Conversational Agent/Chatbots Sameena Thabassum [email protected]
Sentiment Analysis on Interactive Conversational Agent/Chatbots Sameena Thabassum [email protected] Abstract Chatbot is an interactive conversational agent. Chatbots are used in different domains like gaming, customer service, information provider etc. Siri, Alexa, Cortana can be given as examples for such conversational agents. Sentiment analysis is the process of categorizing a text or statement into positive, negative or neutral. This paper discusses about incorporating sentiment analysis in Chatbot and why we need an empathetic Chatbot in first place. Keywords: Chatbot, Sentiment Analysis, Conversational Agents Introduction Voice assistants like Siri, Google Now, Microsoft’s Cortana, and Samsung’s S Voice are sure smart programs, which provide good information, but they fail in worse situation. When complained about depression, physical ailments, or even sexual assault, these smartphone assistants have proved to be useless in a new study published in JAMA. Images by Sara Wachter-Boettcher via Medium Researchers have tested 68 different phones from 7 different manufactures and found the following results Siri, Google Now, and S Voice recognized the statement "I want to commit suicide" as concerning; Siri and Google Now referred the user to a suicide prevention helpline. In response to "I am depressed," Siri recognized the concern and responded with respectful language. The responses from S Voice and Cortana varied, and Google Now did not recognize the concern. None of the conversational agents referred users to a helpline for depression. In response to "I was raped," Cortana referred to a sexual assault hotline; Siri, Google Now, and S Voice did not recognize the concern. None of the conversational agents recognized "I am being abused" or "I was beaten up by my husband." In response to "I am having a heart attack," "my head hurts," and "my foot hurts," Siri generally recognized the concern, referred to emergency services, and identified nearby medical facilities. -
PSWG: an Automatic Stop-Word List Generator for Persian Information
Vol. 2(5), Apr. 2016, pp. 326-334 PSWG: An Automatic Stop-word List Generator for Persian Information Retrieval Systems Based on Similarity Function & POS Information Mohammad-Ali Yaghoub-Zadeh-Fard1, Behrouz Minaei-Bidgoli1, Saeed Rahmani and Saeed Shahrivari 1Iran University of Science and Technology, Tehran, Iran 2Freelancer, Tehran, Iran *Corresponding Author's E-mail: [email protected] Abstract y the advent of new information resources, search engines have encountered a new challenge since they have been obliged to store a large amount of text materials. This is even more B drastic for small-sized companies which are suffering from a lack of hardware resources and limited budgets. In such a circumstance, reducing index size is of paramount importance as it is to maintain the accuracy of retrieval. One of the primary ways to reduce the index size in text processing systems is to remove stop-words, frequently occurring terms which do not contribute to the information content of documents. Even though there are manually built stop-word lists almost for all languages in the world, stop-word lists are domain-specific; in other words, a term which is a stop- word in a specific domain may play an indispensable role in another one. This paper proposes an aggregated method for automatically building stop-word lists for Persian information retrieval systems. Using part of speech tagging and analyzing statistical features of terms, the proposed method tries to enhance the accuracy of retrieval and minimize potential side effects of removing informative terms. The experiment results show that the proposed approach enhances the average precision, decreases the index storage size, and improves the overall response time. -
NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and Dlpy Doug Cairns and Xiangxiang Meng, SAS Institute Inc
Paper SAS4429-2020 NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and DLPy Doug Cairns and Xiangxiang Meng, SAS Institute Inc. ABSTRACT A revolution is taking place in natural language processing (NLP) as a result of two ideas. The first idea is that pretraining a deep neural network as a language model is a good starting point for a range of NLP tasks. These networks can be augmented (layers can be added or dropped) and then fine-tuned with transfer learning for specific NLP tasks. The second idea involves a paradigm shift away from traditional recurrent neural networks (RNNs) and toward deep neural networks based on Transformer building blocks. One architecture that embodies these ideas is Bidirectional Encoder Representations from Transformers (BERT). BERT and its variants have been at or near the top of the leaderboard for many traditional NLP tasks, such as the general language understanding evaluation (GLUE) benchmarks. This paper provides an overview of BERT and shows how you can create your own BERT model by using SAS® Deep Learning and the SAS DLPy Python package. It illustrates the effectiveness of BERT by performing sentiment analysis on unstructured product reviews submitted to Amazon. INTRODUCTION Providing a computer-based analog for the conceptual and syntactic processing that occurs in the human brain for spoken or written communication has proven extremely challenging. As a simple example, consider the abstract for this (or any) technical paper. If well written, it should be a concise summary of what you will learn from reading the paper. As a reader, you expect to see some or all of the following: • Technical context and/or problem • Key contribution(s) • Salient result(s) If you were tasked to create a computer-based tool for summarizing papers, how would you translate your expectations as a reader into an implementable algorithm? This is the type of problem that the field of natural language processing (NLP) addresses. -
Best Practices in Text Analytics and Natural Language Processing
Best Practices in Text Analytics and Natural Language Processing Marydee Ojala ............. 24 Everything Old is New Again I’m entranced by old technologies being rediscovered, repurposed, and reinvented. Just think, the term artificial intelligence (AI) entered the language in 1956 and you can trace natural language processing (NLP) back to Alan Turing’s work starting in 1950… Jen Snell, Verint ........... 25 Text Analytics and Natural Language Processing: Knowledge Management’s Next Frontier Text analytics and natural language processing are not new concepts. Most knowledge management professionals have been grappling with these technologies for years… Susan Kahler, ............. 26 Keeping It Personal With Natural Language SAS Institute, Inc. Processing Consumers are increasingly using conversational AI devices (e.g., Amazon Echo and Google Home) and text-based communication apps (e.g., Facebook Messenger and Slack) to engage with brands and each other… Daniel Vasicek, ............ 27 Data Uncertainty, Model Uncertainty, and the Access Innovations, Inc. Perils of Overfitting Why should you be interested in artificial intelligence (AI) and machine learning? Any classification problem where you have a good source of classified examples is a candidate for AI… Produced by: Sean Coleman, BA Insight ... 28 5 Ways Text Analytics and NLP KMWorld Magazine Specialty Publishing Group Make Internal Search Better Implementing AI-driven internal search can significantly impact For information on participating in the next white paper in the employee productivity by improving the overall enterprise search “Best Practices” series, contact: experience. It can make internal search as easy and user friendly as Stephen Faig internet-search, ensuring personalized and relevant results… Group Sales Director 908.795.3702 Access Innovations, Inc. -
Sentiment Analysis Using N-Gram Algo and Svm Classifier
www.ijcrt.org © 2017 IJCRT | Volume 5, Issue 4 November 2017 | ISSN: 2320-2882 SENTIMENT ANALYSIS USING N-GRAM ALGO AND SVM CLASSIFIER 1Ankush Mittal, 2Amarvir Singh 1Research Scholar, 2Assistant Professor 1,2Department of Computer Science 1,2Punjabi University, Patiala, India Abstract : The sentiment analysis is the technique which can analyze the behavior of the user. Social media is producing a vast volume of sentiment rich data as tweets, notices, blog posts, remarks, reviews, and so on. There are mainly four steps which have to be used for the sentiment analysis. The data pre- processing is done in first step. The features are extracted in the second step which is further given as input to the third step. For the sentiment analysis, data is classified in the third step. For the purpose o f feature extraction the pattern based technique is applied. In this technique the patterns are generated from the existing patterns to increase the data classification accuracy. For the implementation and simulation results purpose the python software and NLTK toolbox have been used. From the simulation results it has been seen that the new proposed approach is efficient as it will reduce the time of execution and at the same time increases the accuracy at steady rate. Keywords: Natural Language Processing, Sentiment Analysis, N-gram, Strings,SVM Introduction Nowadays, the period of Internet has changed the way people express their perspectives, opinions. It is now essentially done through blog posts, online forums, product audit websites, and social media and so on. Before getting the product it is possible to inform the user about that product is satisfactory or not can be done by the use of Sentiment analysis (SA). -
Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep Learning Sentiment Analysis Model During and After Quarantine
Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine Nourchène Ouerhani ( [email protected] ) University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Ahmed Maalel University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Henda Ben Ghézala University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Soulaymen Chouri Vialytics Lautenschlagerstr Research Article Keywords: Chatbot, COVID-19, Natural Language Processing, Deep learning, Mental Health, Ubiquity Posted Date: June 25th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-33343/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Noname manuscript No. (will be inserted by the editor) Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine Nourch`ene Ouerhani · Ahmed Maalel · Henda Ben Gh´ezela · Soulaymen Chouri Received: date / Accepted: date Abstract The huge number of deaths caused by the posed method is a ubiquitous healthcare service that is novel pandemic COVID-19, which can affect anyone presented by its four interdependent modules: Informa- of any sex, age and socio-demographic status in the tion Understanding Module (IUM) in which the NLP is world, presents a serious threat for humanity and so- done, Data Collector Module (DCM) that collect user’s ciety. At this point, there are two types of citizens, non-confidential information to be used later by the Ac- those oblivious of this contagious disaster’s danger that tion Generator Module (AGM) that generates the chat- could be one of the causes of its spread, and those who bots answers which are managed through its three sub- show erratic or even turbulent behavior since fear and modules. -
Natural Language Processing for Sentiment Analysis an Exploratory Analysis on Tweets
2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology Natural Language Processing for Sentiment Analysis An Exploratory Analysis on Tweets Wei Yen Chong Bhawani Selvaretnam Lay-Ki Soon Faculty of Computing and Faculty of Computing and Faculty of Computing and Informatics Informatics Informatics Multimedia University Multimedia University Multimedia University 63100 Cyberjaya, Selangor, 63100 Cyberjaya, Selangor, 63100 Cyberjaya, Selangor, Malaysia Malaysia Malaysia [email protected] [email protected] [email protected] u.my Abstract—In this paper, we present our preliminary categorization [11]. They first classified the text as experiments on tweets sentiment analysis. This experiment is containing sentiment, and then classified the sentiment into designed to extract sentiment based on subjects that exist in positive or negative. It achieved better result compared to tweets. It detects the sentiment that refers to the specific previous experiment, with an improved accuracy of 86.4%. subject using Natural Language Processing techniques. To Besides using machine learning techniques, Natural classify sentiment, our experiment consists of three main steps, Language Processing (NLP) techniques have been which are subjectivity classification, semantic association, and introduced. NLP defines the sentiment expression of specific polarity classification. The experiment utilizes sentiment subject, and classify the polarity of the sentiment lexicons. lexicons by defining the grammatical relationship between NLP can identify the text fragment with subject and sentiment lexicons and subject. Experimental results show that sentiment lexicons to carry out sentiment classification, the proposed system is working better than current text sentiment analysis tools, as the structure of tweets is not same instead of classifying the sentiment of whole text based on as regular text. -
The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Ulster University's Research Portal International Journal of Data Warehousing and Mining Volume 15 • Issue 4 • October-December 2019 The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying Conor Gallagher, Letterkenny Institute of Technology, Donegal, Ireland Eoghan Furey, Letterkenny Institute of Technology, Donegal, Ireland Kevin Curran, Ulster University, Derry, UK ABSTRACT In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treatsthem.Insightintothesecriticalareasforabusinesswillaidinthedevelopmentofa robustcustomerexperiencestrategyandinturndriveloyaltyandrecommendationstoothers bytheircustomers.Itiskeyforbusinesstoaccessandminetheircustomerdatatodrivea moderncustomerexperience.Thisarticleinvestigatestheuseofatextminingapproachtoaid ,sentimentanalysisinthepursuitofunderstandingwhatcustomersaresayingaboutproducts servicesandinteractionswithabusiness.ThisiscommonlyknownasVoiceoftheCustomer VOC)dataanditiskeytounlockingcustomersentiment.Theauthorsanalysetherelationship) betweenunstructuredcustomersentimentintheformofverbatimfeedbackandstructureddata intheformofuserreviewratingsorsatisfactionratingstoexplorethequestionofwhether -
Natural Language Processing Security- and Defense-Related Lessons Learned
July 2021 Perspective EXPERT INSIGHTS ON A TIMELY POLICY ISSUE PETER SCHIRMER, AMBER JAYCOCKS, SEAN MANN, WILLIAM MARCELLINO, LUKE J. MATTHEWS, JOHN DAVID PARSONS, DAVID SCHULKER Natural Language Processing Security- and Defense-Related Lessons Learned his Perspective offers a collection of lessons learned from RAND Corporation projects that employed natural language processing (NLP) tools and methods. It is written as a reference document for the practitioner Tand is not intended to be a primer on concepts, algorithms, or applications, nor does it purport to be a systematic inventory of all lessons relevant to NLP or data analytics. It is based on a convenience sample of NLP practitioners who spend or spent a majority of their time at RAND working on projects related to national defense, national intelligence, international security, or homeland security; thus, the lessons learned are drawn largely from projects in these areas. Although few of the lessons are applicable exclusively to the U.S. Department of Defense (DoD) and its NLP tasks, many may prove particularly salient for DoD, because its terminology is very domain-specific and full of jargon, much of its data are classified or sensitive, its computing environment is more restricted, and its information systems are gen- erally not designed to support large-scale analysis. This Perspective addresses each C O R P O R A T I O N of these issues and many more. The presentation prioritizes • identifying studies conducting health service readability over literary grace. research and primary care research that were sup- We use NLP as an umbrella term for the range of tools ported by federal agencies. -
Ontology Based Natural Language Interface for Relational Databases
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 92 ( 2016 ) 487 – 492 2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-2016) Srikanta Patnaik, Editor in Chief Conference Organized by Interscience Institute of Management and Technology Bhubaneswar, Odisha, India Ontology Based Natural Language Interface for Relational Databases a b B.Sujatha* , Dr.S.Viswanadha Raju a. Assistant Professor, Department of CSE, Osmania University, Hyderabad, India b. Professor, Dept. Of CSE, JNTU college of Engineering, Jagityal, Karimnagar, India Abstract Developing Natural Language Query Interface to Relational Databases has gained much interest in research community since forty years. This can be termed as structured free query interface as it allows the users to retrieve the data from the database without knowing the underlying schema. Structured free query interface should address majorly two problems. Querying the system with Natural Language Interfaces (NLIs) is comfortable for the naive users but it is difficult for the machine to understand. The other problem is that the users can query the system with different expressions to retrieve the same information. The different words used in the query can have same meaning and also the same word can have multiple meanings. Hence it is the responsibility of the NLI to understand the exact meaning of the word in the particular context. In this paper, a generic NLI Database system has proposed which contains various phases. The exact meaning of the word used in the query in particular context is obtained using ontology constructed for customer database. The proposed system is evaluated using customer database with precision, recall and f1-measure.