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1 |
Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales
انتشار مصرف منفی مصرف کننده آنلاین: کمی کردن تأثیر کم عملکرد زنجیره تامین بر فروش خودروهای سواری-2021 The paper presents a text analytics framework that analyses online reviews to explore how consumer-perceived negativity corresponding to the supply chain propagates over time and how it affects car sales. In particular, the framework integrates aspect-level sentiment analysis using SentiWordNet, time-series decomposition, and bias- corrected least square dummy variable (LSDVc) – a panel data estimator. The framework facilitates the business community by providing a list of consumers’ contemporary interests in the form of frequently discussed product attributes; quantifying consumer-perceived performance of supply chain (SC) partners and comparing the competitors; and a model assessing various firms’ sales performance. The proposed framework demonstrated to the automobile supply chain using a review dataset received from a renowned car-portal in India. Our findings suggest that consumer-voiced negativity is maximum for dealers and minimum for manufacturing and assembly related features. Firm age, GDP, and review volume significantly influence car sales whereas the sentiments corresponding to SC partners do not. The proposed research framework can help the manufacturers in inspecting their SC partners; realising consumer-cited critical car sales influencers; and accurately predicting the sales, which in turn can help them in better production planning, supply chain management, marketing, and consumer relationships. Keywords: Supply chain management | Sentiment analysis | Panel data modelling | Online reviews | Natural language processing |
مقاله انگلیسی |
2 |
Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions
تجزیه و تحلیل احساسات در توییت های خطاب به یک حساب توییتر خاص دامنه: مقایسه عملکرد مدل و توضیح پذیری پیش بینی ها-2021 Many institutions and companies find it valuable to know how people feel about their ventures; hence, scientific
research in sentiment analysis has been intensely developed over time. Automated sentiment analysis can be
considered as a machine learning (ML) prediction task, with classes representing human affective states. Due to
the rapid development of ML and deep learning (DL), improvements in automatic sentiment analysis perfor-
mance are achieved almost every year. Since 2013, Semantic Evaluation (SemEval) has hosted a worldwide
community-acknowledged competition that allows for comparisons of recent innovations. The sentiment analysis
tasks focus on assessing sentiment in Twitter posts authored by various publishers and addressing multiple
subjects. Our study aimed to compare selected popular and recent natural language processing methods using a
new data set of Twitter posts sent to a single Twitter account. For improved comparability of our experiments
with SemEval, we adopted their metrics and also deployed our models on data published for SemEval-2017. In
addition, we investigated if an unsupervised ML technique applied for the detection of topics in tweets can be
leveraged to improve the predictive performance of a selected transformer model. We also demonstrated how a
recent explainable artificial intelligence technique can be used in Twitter sentiment analysis to gain a deeper
understanding of the models’ predictions. Our results show that the most recent DL language modeling approach
provides the highest quality; however, this quality comes at reduced model transparency. keywords: پردازش زبان طبیعی | یادگیری عمیق | تجزیه و تحلیل احساسات | فراگیری ماشین | توضیح پذیری | توییتر | Natural language processing | Deep learning | Sentiment analysis | Machine learning | Explainability | Twitter |
مقاله انگلیسی |
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Does government information release really matter in regulating contagionevolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics
آیا انتشار اطلاعات دولتی در تنظیم تکامل منفی احساسات منفی در مواقع اضطراری عمومی اهمیت دارد؟ از منظر تجزیه و تحلیل داده های بزرگ شناختی-2020 The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance.
The traditional government information release strategies ignored the negative emotion evolution
mechanism. Focusing on the information release policies from the perspectives of the government during public
emergency events, by using cognitive big data analytics, our research applies deep learning method into news
framing framework construction process, and tries to explore the influencing mechanism of government information
release strategy on contagion-evolution of negative emotion. In particular, this paper first uses
Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergenciesoriented
emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs
an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment,
it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than
the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the
emotional lexicon, using the emotion computing method proposed. And the empirical results show that the
algorithm is feasible and effective. The experimental results showed that this model could effectively conduct
fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification.
The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence
and weak department coordination, the existing government information release strategies had a significant
negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative
emotions effectively. These research results will provide theoretical implications and technical supports for the
social governance. And it could also help to establish negative emotion management mode, and construct a new
pattern of the public opinion guidance. Keywords: Government information release | Cognitive big data analytics | E-government | Sentiment analysis | Public emergency events |
مقاله انگلیسی |
4 |
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020 Big data generated by social media stands for a valuable source of information, which offers an
excellent opportunity to mine valuable insights. Particularly, User-generated contents such as
reviews, recommendations, and users’ behavior data are useful for supporting several marketing
activities of many companies. Knowing what users are saying about the products they bought or
the services they used through reviews in social media represents a key factor for making decisions.
Sentiment analysis is one of the fundamental tasks in Natural Language Processing.
Although deep learning for sentiment analysis has achieved great success and allowed several
firms to analyze and extract relevant information from their textual data, but as the volume of
data grows, a model that runs in a traditional environment cannot be effective, which implies the
importance of efficient distributed deep learning models for social Big Data analytics. Besides, it
is known that social media analysis is a complex process, which involves a set of complex tasks.
Therefore, it is important to address the challenges and issues of social big data analytics and
enhance the performance of deep learning techniques in terms of classification accuracy to obtain
better decisions.
In this paper, we propose an approach for sentiment analysis, which is devoted to adopting
fastText with Recurrent neural network variants to represent textual data efficiently. Then, it
employs the new representations to perform the classification task. Its main objective is to enhance
the performance of well-known Recurrent Neural Network (RNN) variants in terms of
classification accuracy and handle large scale data. In addition, we propose a distributed intelligent
system for real-time social big data analytics. It is designed to ingest, store, process,
index, and visualize the huge amount of information in real-time. The proposed system adopts
distributed machine learning with our proposed method for enhancing decision-making processes.
Extensive experiments conducted on two benchmark data sets demonstrate that our
proposal for sentiment analysis outperforms well-known distributed recurrent neural network
variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory
(BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach
using the three different deep learning models. The results show that our proposed approach
is able to enhance the performance of the three models. The current work can provide
several benefits for researchers and practitioners who want to collect, handle, analyze and visualize
several sources of information in real-time. Also, it can contribute to a better understanding
of public opinion and user behaviors using our proposed system with the improved
variants of the most powerful distributed deep learning and machine learning algorithms.
Furthermore, it is able to increase the classification accuracy of several existing works based on
RNN models for sentiment analysis. Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics |
مقاله انگلیسی |
5 |
Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning
بررسی ترجیحات مصرف کننده در طرح های محصول با تجزیه و تحلیل نظرات شبکه های اجتماعی با استفاده از استدلال مشهود-2020 The rapid growth of e-commerce and social networking sites has created various challenges for the extraction of
user-generated content (UGC). In the era of big data, customer opinions from social media are utilized for
investigating consumer preferences to support product redesigns.
Opinion mining, including the various automatic text classification algorithms using sentiment analysis is a
capable tool to deal with a large amount of comments on the social networking sites. In which, sentiment
analysis is used to determine the contextual polarity within a comment by searching sentimental words.
However, the inconsistency on choosing the sentiment words leads to the inaccurate interpretation of the opinion
strength of sentiment words.
An approach to summarize the UGC from social networking media using fuzzy and ER without the need to
review all the comments is proposed in this paper. The inaccuracy on determination of the polarity of sentiment
words and corresponding opinion strengths is rectified by fuzzy approximation and ER. The result is presented in
ranking therefore the effort for result interpretation significantly reduced.
The incorporation of sentiment analysis with ER to analyze the UGC for product designs is a new attempt in
investigating consumer preferences. The proposed approach is shown to be handy, sufficient, and cost effective
for the product design and re-design, particularly in the preliminary stage.
This project can be further extended by employing alternative fuzzy approximate techniques in the fuzzy-ER
approach to support the sentiment analysis to enhance the accuracy of sentiment values for determining the
distribution assessments of ER. Keywords: Opinion mining | Sentiment analysis | Evidential reasoning | Consumer preferences | Product design |
مقاله انگلیسی |
6 |
TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers
آموزش: تحقیقات هوش مصنوعی بدون رمزگذاری: هنر مبارزه بدون جنگ: علم داده برای محققان کیفی-2020 In this tutorial, we show how to scrape and collect online data, perform sentiment analysis, social network
analysis, tribe finding, and Wikidata cross-checks, all without using a single line of programming code. In a stepby-
step example, we use self-collected data to perform several analyses of the glass ceiling. Our tutorial can serve
as a standalone introduction to data science for qualitative researchers and business researchers, who have
avoided learning to program. It should also be useful for experienced data scientists who want to learn about the
tools that will allow them to collect and analyze data more easily and effectively. Keywords: Twitter | Data scraping | Sentiment analysis | Tribe finding | Wikidata |
مقاله انگلیسی |
7 |
Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections
آیا تحلیل های توییتر می توانند نتیجه انتخابات را پیش بینی کنند؟ بینشی از انتخابات مجلس پنجم 2017-2020 Since the beginning of this decade, there has seen an exponential growth in number of internet users using social
media, especially Twitter for sharing their views on various topics of common interest like sports, products,
politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big
data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper
takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and
applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful
information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and
have proposed a new seat forecasting method to accurately predict the number of seats that a political party is
likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner
and that in fact was the outcome, when results got declared. Keywords: Analytics | Election prediction | Social media | Natural language processing | Machine learning | Sentiment analysis | Twitter |
مقاله انگلیسی |
8 |
If I give you my emotion, what do I get? Conceptualizing and measuring the co-created emotional value of the brand
"اگر احساسات خود را به شما نشان دهم ، چه می توانم دریافت کنم؟" مفهوم سازی و اندازه گیری ارزش عاطفی ایجاد شده از برند-2020 The emotional value of interactions is a pillar construct in the brand value co-creation domain. So far, research
has neglected the search for a measure adequately considering emotional-based joint interactions. Thanks to a
netnographic sentiment analysis of 7605 brand-users’ interactions retrieved from 18 Twitter brand profiles, this
paper advances knowledge on brand co-creation and introduces a new concept in the marketing domain, the cocreated
emotional value of the brand, operationalised through the Emotional Co-Creation Score (ECCS). The paper
reveals that different emotional experiential paths can be generated by the simultaneous interaction between the
brand and its consumers. In particular, it shows that some sectors co-create more than others. Furthermore,
brands provide more positive emotions than consumers and, when dealing with consumers’ extreme polar
emotions, they compensate consumers’ emotions by calibrating the ECCS, which is not influenced by the frequency
of Likes, and only marginally influenced by the frequency of interactions. Keywords: Brand | Co-creation | Emotional value | Sentiment analysis | Brand measure | Marketing management |
مقاله انگلیسی |
9 |
An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020 Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas
emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the
significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in
India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles.
But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV).
This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles.
The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for
them), marketers (for determining what features should be advertised) and manufacturers (for deciding what
features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural
Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data
platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over
traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.)
due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social
media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN
yield better results in-compare to others. The proposed optimal model will help consumers, designers and
manufacturers in their decision-making capabilities to choose, design and manufacture EV. Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India |
مقاله انگلیسی |
10 |
Negation scope detection for sentiment analysis: A reinforcement learning framework for replicating human interpretations
تشخیص دامنه منفی برای تجزیه و تحلیل احساسات: یک چارچوب یادگیری تقویت کننده برای تکرار تفسیرهای انسانی-2020 Textual materials represent a rich source of information for improving the decision-making
of people, businesses and organizations. However, for natural language processing (NLP), it
is difficult to correctly infer the meaning of narrative content in the presence of negations.
The reason is that negations can be formulated both explicitly (e.g., by negation words such
as ‘‘not”) or implicitly (e.g., by expressions that invert meanings such as ‘‘forbid”) and that
their use is further domain-specific. Hence, NLP requires a dynamic learning framework for
detecting negations and, to this end, we develop a reinforcement learning framework for
this task. Formally, our approach takes document-level labels (e.g., sentiment scores) as
input and then learns a negation policy based on the document-level labels. In this sense,
our approach replicates human perceptions as provided by the document-level labels and
achieves a superior prediction performance. Furthermore, it benefits from weak supervision;
meaning that the need for granular and thus expensive word-level annotations, as
in prior literature, is replaced by document-level annotations. In addition, we propose an
approach to interpretability: by evaluating the state-action table, we yield a novel form
of statistical inference that allows us to test which linguistic cues act as negations. Keywords: Unstructured data| Information processing | Decision-making | Natural language processing | Reinforcement learning | Negations |
مقاله انگلیسی |