DOCSLIB.ORG

For Unigram and Bigram Tweets

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
For Unigram and Bigram Tweets International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 235-241 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ A SVM Based Sentiment Analysis Method (SBSAM) for Unigram and Bigram Tweets S. Geetha Dr. R. Kaniezhil Assistant Professor Principal Department of Computer Science MIT College of Arts & Science for Women Muthurangam Govt. Arts College(Autonomous) Musiri - 621211 Vellore- 632001 Email:[email protected] Email: [email protected] I. INTRODUCTION Abstract: Social media sites are places where citizens voice their opinions without fear. There is growing sense of Social media sites are places where citizens voice urgency to understand public opinions because of the viral their opinions without fear. There is growing sense of nature of social media. Making sense of these mass conversations for interacting meaningfully is in demand. urgency to understand citizens opinions because of the Sentiment analysis (SA) is the study where sentiments are viral nature of social media. Making sense of these computed for a conclusion. SA can apply anywhere as mass conversations for interacting meaningfully are public opinion on variety of subjects can be assessed. increasing in demand. Sentiment analysis is the study Stock markets fluctuations are in a way based on public where sentiments are computed for a conclusion. opinion. SA can help optimize promotional strategies. In Sentiments Entity can be classified as product, person, marketing tactics, sentiment analysis can help fit brand, event, concept. Analysis of sentiments is a marketing campaigns for target audiences. Success of a dynamic and challenging task [1]. This has created the campaign also lies in positive discussions amongst demand for Information filtering systems (IFS) dealing customers, where sentiment analysis plays a major role. Moreover, the volume of digital information on the with information overload (Recommender Systems) [2]. Internet has been responsible in increasing access times on Information retrieval systems have been able to satisfy items of interest for users. This voluminous information these analysis by prioritization or personalization of has to be filtered, prioritized and delivered to users to interesting information found on the internet, but are satisfy their search requirements for recommendations. scarce in numbers. These systems also called SA can help quality refinements and further research. recommender systems, filter vital information This paper underlines the need for sentimental analysis fragments according to user‟s preferences or interest or and recommender systems based on sentimental analysis behaviors from the dynamically generated internet for users. Further, it proposes a Sentiment Analysis information [3]. Recommender systems are beneficial Method based on SVM data mining technique (SBSAM) on unigram and bigram tweets. SBSAM aims to fulfill to service providers and users alike and can predict a sentimental analysis with speed for recommender systems user preferences on items based on their profile[4]. The useful to end users. systems help improve qualitative decision making processes [5]. Millions of users share their opinions on 235 International Journal of Pure and Applied Mathematics Special Issue various topics on social networking sites. The limit of randomly, start to plan strategically for promoting 140 characters for messages in these micro-blogging their products based on public feedbacks. sites forces the users to be concisely expressive in their Managerial viewpoints can based on the analysis of comments or opinions. customers chit chat about brands and used to post Twitter, a social media site has over two hundred full brand information to gauge how customers million users, where more than 50% are active. More perceive the product or brand. It is a place for than fifty percent if Twitter users log on generating optimization of marketing strategies. By listening more than 200 million tweets per day [6]. Public to customers feeling high level decisions can be sentiments reflect on tweets which can be analyzed. adjusted to meet customer needs. Tactical These expressed sentiments are vital to firms for marketing can be effected by building short-term finding out responses about their products or to marketing campaigns for customer requirements. politicians for predicting election results or investors By continuously having sentiment analysis in for predicting stock prices. Studies classified sentiments place, campaigns can be adjusted to fit more of the as positive and negative based on unigrams, features target audiences. and a tree kernel. Unigrams were analyzed for more Measuring ROI of Campaigns: Success of than hundred features and joint to a tree [7]. Tweet marketing campaign may be measured by the sentiment namely objective and subjective were increase in the number of followers or comments automatically identified and separated in [8]. In the or likes. The true success lies in positive second step subjective message‟s polarity was discussions amongst customers on campaigns. determined. Sentiments in linguistic features were Sentiment analysis can relate and count positive or studied in [9] and evaluated the usefulness of lexical negative discussions that have occurred amongst resources used in informal and creative texts of tweets. audiences. By combining quantitative and e Parts of speech were found to be less useful in qualitative measurements, the true ROI of analyzing micro-blogging tweets [10]. They extracted marketing campaigns can be measured. features from lexicons along with micro-blogging Developing product quality: Sentiment analysis features and classified automatically Twitter sentiments helps completion of market research by assessing The messages were classified as positive or negative. customer opinions on products/services and ways Their framework had two distinct components namely to align products/services. Products are judged by classifiers and feature extractors which achieved higher presentations like pricing and package design. accuracy with machine learning algorithms on Ideas on developing product quality and sentiment analysis. presentation can be derived directly from customer Thus, sentiment analysis from user tweets can be of opinions. Sentimental Analysis of opinions is an help in many areas. Manual extraction of such useful alternative to structured and planned surveys. It can information from this voluminous data is almost also be used to fulfill customer complaints. impossible. Sentiments can be categorized into positive Improving customer service: Customers who buy or negative or neutral which helps determine attitude of products try to be loyal to the brand as long as the general public on topics. This paper aims to detect possible and influence the products or brands sentiments from tweets as accurately as possible. The positively. Hence, it is important to have the best technique has three main parts, where preprocessing customer service in place for keeping current tweets text is the first part, in the second part, required customers happy. On-time delivery, response on features are extracted and in the final part machine social media and reimbursement for erroneous learning algorithm SVM is used for classification [11]. product are some of the cues for good customer Thus, this paper proposes a novel method for service. Sentiment analysis can identify negative sentimental analysis called, SBSAM based on SVM discussions and thus be an alert on improvements. classifier, for accurate and automatic sentiment analysis Faster responses draws customer attention and of twittered tweets. eventually their satisfaction. Sentiment analysis is a part of social complaint listener that helps avoid A. Benefits of Sentimental Analysis customers feeling ignored and angry like Finnair responding to customer‟s twitter. Sentimental Analysis can be used by celebrities, Crisis management: Constant monitoring of organizations, governments and individuals to get social media conversations also helps minimize public opinions voiced on social media. Some of the damage of crisis due to online communications. A major benefits of sentimental analysis is detailed below product's bad quality, environmental worries , bad Fine-tune marketing strategies: Many customer service can create a catastrophe in organization actively use social media to promote emerging markets. Sentiment analysis can detect their products. Even companies which post 236 International Journal of Pure and Applied Mathematics Special Issue manifestations from customer discussions and thus names, they are prone to poor results. Mostly names get help manage crisis early. misclassified. Lead generation: Accurate sentiment analysis can Identifying the user’s preferences: Sentiment result in better marketing campaigns and with analysis in political arena can forecast election customer service, and quality, it can result in new outcomes. Categorizing users into groups like left, right leads. Happy customers, act as brand ambassadors and independent and gaining information about users and bring in new customers. Also, customer needs, helps improve the social media-based predictions, but their views can give create a path for better selling are not too easy [17]. content that can attract new customers. Hashtags: Sentiment analysis on emotions and Sales Revenue: The biggest benefit of sentiment emoticons can identify emotions for monitoring [18]. analysis lies
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full 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
    [Show full text]
  • Automated Citation Sentiment Analysis Using High Order N-Grams: a Preliminary Investigation
    Turkish Journal of Electrical Engineering & Computer Sciences Turk J Elec Eng & Comp Sci (2018) 26: 1922 – 1932 http://journals.tubitak.gov.tr/elektrik/ © TÜBİTAK Research Article doi:10.3906/elk-1712-24 Automated citation sentiment analysis using high order n-grams: a preliminary investigation Muhammad Touseef IKRAM1;∗,, Muhammad Tanvir AFZAL1, Naveed Anwer BUTT2, 1Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan 2Department of Computer Science, University of Gujrat, Gujrat, Pakistan Received: 03.12.2017 • Accepted/Published Online: 02.02.2018 • Final Version: 27.07.2018 Abstract: Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author’s sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral. In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus.
    [Show full text]
  • Sentiment Analysis & Natural Language: Processing Techniques
    MACHINE LEARNING Sentiment Analysis & Natural Language: Processing Techniques for Capital Markets & Disclosure By Nicolas H.R. Dumont Application of machine learning, artificial the compilation of information taking differ- intelligence, and advanced analytics to big data ent forms, stemming from multiple sources, influences nearly all industries today. The secu- produced at different times.3 This complexity rities markets are no exception. Issuers use these and volume calls for techniques that enable tools for marketing, product development, and the capture, storage, processing, and analysis operations; investors harness data in search of of such information. In this way, “big data” trading insights; and regulators monitor com- is more than just a lot of information; it rep- pliance and detect risks. Markets themselves resents a new frame in which information is create a wealth of data that perpetuates a virtu- collected, connected, and used. ous cycle of information generation, reliance and analysis. While investors have long sought out new forms of information to enhance returns, the To date, discussions surrounding technol- digital age generates exponentially more data ogy and finance focus on how each player uses from countless new sources. Investors can har- innovation to achieve its own goals, whether ness satellite images to measure customer cars streamlining operations, increasing returns, or in parking lots, and can “scrape” issuer Web detecting fraud.1 There is considerably less dis- sites for more information than what was per- cussion about how these developments born of haps intended for public consumption. In fact, the Internet era influence (or should influence) data is now so big—in terms of relevance and issuers.
    [Show full text]
  • Natural Language Processing, Sentiment Analysis and Clinical Analytics
    Natural Language Processing, Sentiment Analysis and Clinical Analytics Adil Rajput ([email protected]) Assistant Professor, Information System Department, Effat University An Nazlah Al Yamaniyyah, Jeddah 22332, Jeddah, Saudi Arabia Abstract Recent advances in Big Data has prompted health care practitioners to utilize the data available on social media to discern sentiment and emotions’ expression. Health Informatics and Clinical Analytics depend heavily on information gathered from diverse sources. Traditionally, a healthcare practitioner will ask a patient to fill out a questionnaire that will form the basis of diagnosing the medical condition. However, medical practitioners have access to many sources of data including the patients’ writings on various media. Natural Language Processing (NLP) allows researchers to gather such data and analyze it to glean the underlying meaning of such writings. The field of sentiment analysis – applied to many other domains – depend heavily on techniques utilized by NLP. This work will look into various prevalent theories underlying the NLP field and how they can be leveraged to gather users’ sentiments on social media. Such sentiments can be culled over a period of time thus minimizing the errors introduced by data input and other stressors. Furthermore, we look at some applications of sentiment analysis and application of NLP to mental health. The reader will also learn about the NLTK toolkit that implements various NLP theories and how they can make the data scavenging process a lot easier. Keywords: Natural Language Processing (NLP), Data scraping, social media analysis, Sentiment Analysis, Helathcare analytics, Clinical Analytics, Machine Learning, Corpus 1. Introduction The Big Data revolution has changed the way scientists approach problems in almost every (if not all) area of research.
    [Show full text]
  • Sentiment Analysis and the Complex Natural Language
    Khan et al. Complex Adapt Syst Model (2016) 4:2 DOI 10.1186/s40294-016-0016-9 REVIEW Open Access Sentiment analysis and the complex natural language Muhammad Taimoor Khan1*, Mehr Durrani2, Armughan Ali2, Irum Inayat3, Shehzad Khalid1 and Kamran Habib Khan4 *Correspondence: taimoor.muhammad@gmail. Abstract com There is huge amount of content produced online by amateur authors, covering a 1 Bahria University, Shangrilla Road, Sector E‑8, Islamabad, large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ senti- Pakistan ments towards a target entity. Machine learning (ML) techniques are frequently used Full list of author information as the natural language data is in abundance and has definite patterns. ML techniques is available at the end of the article adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based tech- niques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer- agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP chal- lenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology.
    [Show full text]