scholarly journals Sentiment Analysis of Impact of Social Platforms on the Market Share of a Company

2019 ◽  
Vol 8 (2) ◽  
pp. 540-543
Keyword(s):  
Market Share ◽  
Computational Linguistics ◽  
Language Processing ◽  
Opinion Mining ◽  
Affective States ◽  
Lexical Resources ◽  
Linguistic Feature ◽  
Utility Evaluation ◽  
The Impact ◽  
A Company

Sentimental analysis is also known as opinion mining or emotion AI. It refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, and study affective states and subjective information. In this paper, Amazon reviews and blogs are analyzed to detect the sentiment using linguistic feature utility. Evaluation of the usefulness of existing lexical resources as well as capturing information about the informal and creative language used in online service platform is done. The goal of this research is to show the impact on the market-share of Vivo in comparison with that of Oppo and highlight the reason for the impact.

scholarly journals Sentiment Analysis on Twitter Data: A Comparative Approach

2021 ◽  
Vol 9 (10) ◽  
pp. 1-12
Author(s):  
Subhadip Chandra ◽  
Randrita Sarkar ◽  
Sayon Islam ◽  
Soham Nandi ◽  
Avishto Banerjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Comparative Approach ◽  
Support Vector ◽  
Affective States ◽  
Twitter Data ◽  
Social Media Platforms

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.

scholarly journals Speech and Opinion Recognition from a Conversation

2020 ◽  
Vol 9 (4) ◽  
pp. 1189-1193
Keyword(s):  
Machine Learning ◽  
Computational Linguistics ◽  
Language Processing ◽  
Opinion Mining ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Accuracy And Precision ◽  
The Subject ◽  
Processing Techniques

Speech Recognition is an interdisciplinary technique used to convert spoken language into text. It is a sub domain of computational linguistics and can be implemented using Machine Learning and Deep Learning Algorithms. Opinion Mining or Sentiment Analysis is a process which enables identifying opinions expressed by an author in a piece of text computationally. This opinion refers to the polarity of the expressed opinion, i.e. positive or negative. Through this research work, we aim to combine these two natural language processing techniques and devise a system that can take speech as the input and determine the sentiment behind the speakers’ words. The subject of the speech input may vary but the end goal is to recognize whether the attitude of the speaker towards the subject was positive or negative. The input will be converted to text and this text will then be classified using several different machine learning techniques. These include Naïve Bayes’ Classifier, Support Vector Machine, Logistic Regression and Decision Trees. After classification, the results for the three classifiers will be predicted and compared. Future scope of the project includes creating an ensemble of these classifiers to get better accuracy and precision of determining the sentiment of the speaker

Infosys Limited: is it still the Indian IT bellwether?

2013 ◽  
Vol 3 (7) ◽  
pp. 1-29
Author(s):  
Arun Kumar ◽  
Meenakshi Nagarajan
Keyword(s):  
Business Model ◽  
Market Share ◽  
International Business ◽  
Value Chain ◽  
Business Environment ◽  
It Industry ◽  
Content Type ◽  
Consulting Services ◽  
The Impact ◽  
A Company

Subject area Strategy. Study level/applicability MBA level. The case can be used primarily for the following courses: strategic management, competitive strategy. It can also be used for courses on: international business, international business environment, business marketing. Case overview Intense competition and a turbulent economic environment posed problems for Infosys, a leading information technology (IT) company in India. Infosys lost market share and its second position in the IT industry to Cognizant. An adverse economic environment affected its clients' IT spending and introduced severe price-based competition in the market. Infosys' business model operated on charging price premium from clients, and the company never compromised on its margins. The company was forced to revaluate, as outsourcing, the main revenue earner for Infosys was experiencing commoditization, and other players were willing to compromise on margins. The Indian IT industry had moved up the value chain and competitors were offering consulting services, where there was huge scope for differentiation. Infosys did not have the requisite resources to compete in this domain. Decline in share prices, negative investor sentiments, downward revision of revenue guidance targets, loss of large clients, higher attrition rates, and visa problems in the US market (Infosys earned more than 60 percent revenues from this market) added worries for the company. In response to these challenges, Infosys initiated Strategy 3.0, wherein the company planned to move up the value chain and offer consulting services and other high-end solutions to clients. This was a shift from its predominantly outsourcing-based revenue model. The company acquired Lodestone to hasten implementation of Strategy 3.0. Initial analysis, however, suggested that Infosys was merely aping Cognizant's well-established strategy. Infosys also needed to tackle perceptual issues regarding its competencies. Expected learning outcomes The instructor can use this case to facilitate the understanding of: the impact of an intensely competitive environment on a company's strategy, how changes in the competitive landscape and business environment can erode sources of competitive advantage for an incumbent, the impact of a client's business environment on the vendor's business, the concept of value chain and analyze how companies in an industry move up in the value chain, the concept of business model, and how environmental changes can impact a hitherto robust business model of a company, evolution of business model over a period of time with changes in the business environment, the internal conflict between ideals and values versus revenues and market share for a company, key resources and capabilities that shape the differential advantage for an IT company, designing and implementing strategic solutions, the evolution of the Indian IT industry. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email [email protected] to request teaching notes.

scholarly journals Mutual sentiment

2020 ◽  
Vol 210 ◽  
pp. 15006
Author(s):  
Olga Maksimenko ◽  
Tatiana Semina ◽  
Alexander Khmelev ◽  
Natalia Dmitrieva
Keyword(s):  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Source Material ◽  
Affective States ◽  
Named Entities ◽  
Proper Nouns ◽  
The Subject ◽  
And Linguistics

Sentiment analysis is a modern task in natural language processing and linguistics. Also referred to as opinion mining, it deals with different kinds of affective states: opinion, emotions, stance and evaluations. Sentiment itself is the polarity of these affective states. Taking analytical articles as source material for the study, several problems should be considered. Firstly, these texts broaden the understanding of the subject of opinion, because it does not coincide with the author of the text in the majority of cases. Secondly, subjects and objects of opinion are entities – words or word combinations with strictly denoted referent. In the paper only Named Entities, that are normally expressed by proper nouns, are considered. This kind of sentiment analysis requires deeper research of possible sentiment relations between entities and of lexical and grammatical influence on these relations. The paper is devoted to the study of the influence of the group of lexemes on opinion structure. The research shows that mutual sentiment can be presented as stable patterns.

যন্ত্র-না (Jantra-Na Not-Machine) Can Only Feel যন্ত্রনা (Jantrana Pain)!

2013 ◽  
pp. 328-345
Author(s):  
Amitava Das ◽  
Björn Gambäck
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Opinion Mining ◽  
Text Analytics ◽  
Emotional Information ◽  
Solution Strategies ◽  
Basic Task

Arguably, the most important difference between machines and humans is that humans have feelings. For several decades researchers have been trying to create methods to simulate sentimentality for machines, and currently Sentiment Analysis is the hottest, most demanding, and rapidly growing task in the language processing field. Sentiment analysis or opinion mining refers to the application of Natural Language Processing, Computational Linguistics, and text analytics to identify and extract sentimental (opinionated, emotional) information in a text. The basic task in sentiment analysis is to classify the polarity of a given text at the document, sentence, or feature/aspect level, that is, to decide whether the expressed sentiment in a document, a sentence, or a feature/aspect is positive (happy), negative (sad), neutral (memorable), and so forth. In this chapter, the authors discuss various challenges and solution strategies for Sentiment Analysis with a particular view to texts in Bangla (Bengali).

scholarly journals The Effect of Using Automated Essay Evaluation on ESL Undergraduate Students’ Writing Skill

2016 ◽  
Vol 6 (5) ◽  
pp. 54 ◽  
Author(s):  
Ebtisam S. Aluthman
Keyword(s):  
Undergraduate Students ◽  
Computational Linguistics ◽  
Language Processing ◽  
Academic Writing ◽  
English Language ◽  
Formative Feedback ◽  
Repeated Measure ◽  
The Impact ◽  
Essay Evaluation ◽  
Automated Essay Evaluation

<p>Advances in Natural Language Processing (NLP) have yielded significant advances in the language assessment field. The Automated Essay Evaluation (AEE) mechanism relies on basic research in computational linguistics focusing on transforming human language into algorithmic forms. The Criterion® system is an instance of AEE software providing both formative feedback and an automated holistic score. This paper aims to investigate the impact of this newly-developed AEE software in a current ESL setting by measuring the effectiveness of the Criterion® system in improving ESL undergraduate students’ writing performance. Data was collected from sixty-one ESL undergraduate students in an academic writing course in the English Language department at Princess Norah bint Abdulruhman University PNU. The researcher employed a repeated measure design study to test the potential effects of the formative feedback and automated holistic score on overall writing proficiency across time. Results indicated that the Criterion® system had a positive effect on the students’ cores on their writing tasks. However, results also suggested that students’ mechanics in writing significantly improved, while grammar, usage and style showed only moderate improvement. These findings are discussed in relation to AEE literature. The paper concludes by discussing the implications of implementing AEE software in educational contexts.</p><p><span><br /></span></p>

The Role of Web Based Accounting Online System 2.0 as the Company's Income and Expense Management

2017 ◽  
Vol 1 (1) ◽  
pp. 44-49
Author(s):  
Nur Azizah ◽  
Dedeh Supriyanti ◽  
Siti Fairuz Aminah Mustapha ◽  
Holly Yang
Keyword(s):  
Financial Management ◽  
Accounting System ◽  
Bank Account ◽  
Web Based ◽  
Online System ◽  
Version 2.0 ◽  
User Friendly ◽  
The Impact ◽  
A Company

In a company, the process of income and expense of money must have a profit-generating goal base. The success of financial management within the company, can be monitored from the ability of the financial management in managing the finances and utilize all the opportunities that exist with as much as possible with the aim to control the company's cash (cash flow) and the impact of generating profits in accordance with expectations. With a web-based online accounting system version 2.0, companies can be given the ease to manage money in and out of the company's cash. It has a user friendly system with navigation that makes it easy for the financial management to use it. Starting from the creation of a company's cash account used as a cash account and corporate bank account on the system, deletion or filing of cash accounts, up to the transfer invoice creation feature, receive and send money. Thus, this system is very effective and efficient in the management of income and corporate cash disbursements.   Keywords:​Accounting Online System, Financial Management, Cash and Bank

PENERAPAN CORPORATE SOCIAL RESPONSIBILITY DALAM PENGELOLAAN WAKAF PRODUKTIF

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Masruchin Masruchin
Keyword(s):  
Environmental Impacts ◽  
Infrastructure Development ◽  
Religious Activities ◽  
Short Term ◽  
Local Society ◽  
Business Units ◽  
Corporate Social ◽  
The Impact ◽  
A Company ◽  
Social And Environmental Impacts

Corporate Social Responsibilityis a concept that a company has various forms of responsibility to all stakeholders including consumers, employees, shareholders, communities and the environment in all aspects of the company's operations that include economic, social, and environmental aspects. Therefore CSR is closely related to "sustainable development", in which a company, in carrying out its activities must base its decisions not only on the impact on economic aspects, such as the level of profits or dividends (profits), but also must consider the social and environmental impacts that arise from that decision, both for the short term and the longer term.Pondok Modern Darussalam Gontor (PMDG), in managing its Productive Waqf by establishing business units which mostly involve workers from the local society around PMDG. They are employed according to their skills. This is a form of implementing CSR in order to help advance and improve the welfare of the local society. The existence of these various business units is one of the educational facilities and as a form of CSR application which is actually intended to educate in the fields of independence, entrepreneurship, sincerity and sacrifice.PMDG involvement in social activities that are useful for the local society such as infrastructure development and village facilities, regeneration of students who are from around PMDG to be able to get higher education with funding from the PMDG, doing guidance to the local society through various religious activities, educational and economic activities is a form of PMDG responsibility to the local society environment and also to all stakeholders such as students, Ustadz, employees, so as to provide social and environmental impacts for the short term and the longer term.Keywords: Corporate Social Responsibilityandproductive waqf.

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