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Certain Investigations on Sentimental Analysis Architecture and Tools International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-5C, February 2019 Certain Investigations on Sentimental Analysis Architecture and Tools R. P. Ramkumar, Sanjeeva Polepaka method was tested on blog posts) from social media. There Abstract— The sentiment is defined as the feeling(s) about the were three steps in determining the analysis. In step 1, all the review or comment. The Sentimental Analysis aims to determine product features were extracted. In step 2, simultaneously the attitude of content or product for a period at a given moment. separation of text and emoticons and extraction of opinion Later, these observations are categorized as negative, neutral, words took place. The categorization of opinion words such positive and sometimes no sentiment(s) at all. The review(s) or comment(s) on a concern product is beneficial for the companies as positive, neutral and negative words was done in step 3. to prioritize the issues, narrow down the problems to be solved and Results concluded that people use emoticons to express their to explore the scenarios for success. This article deals with the feelings in natural language text. study of sentimental analysis or opinion mining architecture and Vivekanandan and Josephine [5] constructed an automated tools used for Sentimental Analysis for the naive users. framework called Review Opinion Mining (ROM) to determine the opinion about the online products. The ROM Keywords: Opinion Mining, Tweets, Opinion Polarities, method has five steps namely (a) Data preprocessing, (b) Crawling, Sentimental Analysis, Twitter Statistics. Aspect extraction, (c) Identification of opinion, (d) Polarity 1. INTRODUCTION identification and (e) Summarization of features. This method was tested on unstructured data to extract the In the technological era, others opinion about the product viewer’s opinion about the products. Though the ROM influences the decision-making process. One or two decades framework analyzed product reviews in a timely and efficient before to get the review about the product(s), we relied manner, it failed to adhere to appropriate algorithms for mostly on relatives and friends. However, in the modern determining the summary report. world, opinion feedback from diverse people may be sought Khandelwal, Mishra and Mishra [6] implemented an over the internet. Before buying the product, people use to analyzer to classify twitter comments as positive, negative look the website(s) for review about a particular product. and neutral. This workflow model initiates with tweet data Similarly, organizations use to get feedback about the identification followed by the data preprocessing and training services and the products for the customers. The identifying data definition. Later, parsing of tweets was carried out by and extracting processes generate the subjective information choosing an efficient algorithm. Subsequently, the training using text analysis, natural language processing and and evaluation follow to classify the comments as subjective computational linguistics is referred to as Sentimental and objective cases, with the test data. Authors concluded Analysis or Opinion Mining. Nowadays, this type of that machine learning algorithms such as Support Vector sentimental analysis becomes more popular to monetize the Machines (SUM) and Naïve Bayes were widely used to products. The following shows the organization of the paper: analyze the tweets even though the algorithm suffers Section 2 deals with related works about opinion mining. limitations. Usage of sentimental analysis and its architecture is Kim and Kim [7] presented a case study on nuclear power discussed in Sections 3 and 4, respectively. Section 5 draws using opinion mining on Twitter. There were four phases or the conclusion and future work. stages in analyzing the tweets, namely (a) Crawling of tweets, (b) Text preprocessing, (c) Constructing the dictionary with 2. RELATED WORKS sentiment words and (d) Predicting the feelings with tweets. The following section shows the related work regarding Locoy spider is the crawling tool used to identify the terms the opinion mining framework. “nuclear power” or simply the “nuclear” in the Korean Meenambigai [4] presented a product based opinion language from 2009 to 2013. Among the five years of data, mining to examine the nature of the product. The objective of the first three years were used to construct the dictionary, this work was to categorize the opinion polarities (like a whereas the later year’s data were used for evaluation negative, neutral or positive) of the product. From the purpose. Results of the assessment method were compared product’s opinion statement, sentiment analysis was done with the human evaluator(s) results. Results concluded that and classified as objective, positive and negative. This this method has higher prediction accuracy on analyzing sentiments than the human evaluators. Revised Version Manuscript Received on 22 February, 2019. R. P. Ram Kumar, Professor, Department of Computer Science and Engineering, Malla Reddy Engineering College (Autonomous), Maisammaguda, Secunderabad, India.(Email: [email protected]) Sanjeeva Polepaka, Associate Professor, Department of Computer Science and Engineering, Malla Reddy Engineering College (Autonomous), Maisammaguda, Secunderabad, India. (e-mail:[email protected]) Published By: Blue Eyes Intelligence Engineering Retrieval Number: E10180275C19/19©BEIESP 69 & Sciences Publication International Conference on Advances in Signal Processing, Power, Embedded, Soft Computing, Communication and Control Systems (ICSPECS-2019) | 11th & 12th January 2019 | GPREC, Kurnool, A.P. India 3. MEASURE THE MATTERS USING Similar to Google Alerts, this tool is SENTIMENTAL ANALYSIS useful in tracking the keywords in Social 9 bookmarks, blogs, events, question and According to Katie Delahaye Paine [1], metrics such as Mention likes, shares, onsite engagements, comments and inbound answers, comments, audio and even in links are very helpful in identifying the sentiment of the videos. people or the user engaged with the content. By just having This tool is used to grade the whole marketing funnel with more than 30 the metric counts only gives the false sense or bad branding Marketing 10 metrics including, blog posts, tweets, about the particular product. Apart from the above-listed Grader parameters, when analyzed, there is a possibility of Quality Facebook updates, the number of metrics, which deals with feelings, opinions, shares quality, visitors and much more. repeated tweets made on the particular product, comments, Table 2 shows the pinnacle software for text mining, text rating, satisfaction scores, rating conversation about the analysis and analytics along with the proprietary solutions quality of the product. Figure 1 shows such a typical example [2]. S. Name of the S. chart of sentimental analysis depicted from [1] and Table 1 Name of the Software shows some of the tools used to track and crack the user’s No. Software No. sentiments. Loop Cognitive 1 Abzooba 31 Computing Platform 2 Ai-one 32 Luminoso 3 AlchemyAPI 33 MeaningCloud Angoss Text 4 Analytics 34 Medallia Ascribe Forest 5 Rim's Texual ETL 35 Megaputer 6 Attensity 36 muText Mu Sigma 7 AUTINDEX 37 NetOwl 8 Averbis 38 OpenText 9 AYLIEN 39 Oracle Endeca Oracle Social Cloud - 10 Basis Technology 40 Collective Intellect Figure 1 Sentimental analysis chart (Courtesy [1]) 11 Bitext 41 Pingar Brainspace Table 1 Practical tool for Sentimental Analysis 12 Discovery 42 Provalis Research S. Name of Details 13 Buzzlogix 43 Rapid Miner No the Tool 14 Clarabridge 44 Rocket Text Analytics Used to uncover insight into targeted 1 Meltwater 15 Content Analyst 45 SAP Text Analytics audience 16 Datumbox 46 Saplo Google Very simple method to track the 2 17 DiscoverText 47 SAS Text Analytics Alerts “content marketing” for regular updates 18 Etuma 48 Semantria A useful tool to evaluate the 19 Expert System 49 SIFT competitors, industries and brands to People General 3 explore the exact status regarding Browser 20 Sentiment 50 Smartlogic before, during and after marketing Google Cloud campaigns. 21 Prediction 51 StatSoft A perfect tool to discover the 22 HP Autonomy 52 Synapsify Google prejudiced subscribers and buyers, such 4 IBM Text Analytics as annotations, custom reports, web 23 Analytics 53 Sysomos designs, etc. 24 Indico 54 SYSTRAN Free and subscription-based options of 5 Hootsuite this tools allow to measure and manage 25 Intellexer 55 Taste Analytics the social media networks data directly. 26 Kanjoya 56 Text2data Free graphical tools to explore Twitter Language 6 Tweetstat statistics. Computer Thomson Reuters 27 Corp[oration 57 Open Calais This tool is used to extract the overall likes, fans, along with the user Lexalytics Text Facebook 7 activities, the sum of fresh likes and 28 Analytics 58 Twinword Insights unlikes, tab views, page views, media 29 LingPipe 59 Verint Systems consumption, referrers and lot more. 30 LinguaSys 60 VisualText This tool can measure the activities in 8 Pagelever the Facebook, such as consumed content, shared content on Facebook. Published By: Blue Eyes Intelligence Engineering Retrieval Number: E10180275C19/19©BEIESP 70 & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-5C, February 2019 4. THE ARCHITECTURE OF SENTIMENTAL Computer and Communication Engineering, Vol. 2, Issue 8, pp. 5261-5265, August 2014. ANALYSIS PROCESS 5. Vivekanandan
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