Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward

Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency

Multi-Modal Topic Sentiment Analytics for Twitter

Title from PDF of title page viewed February 1, 2019Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 50-53)Thesis (M.S.)--School of Computing and Engineering, University of Missouri--Kansas City, 2018Sentiment analysis has proven to be very successful in text applications. Social media is also considered a quite rich source to get data regarding user’s behaviors and preference. Identifying social context would make the sentiment analysis more meaningful to the applications. Due to the limited contextual information in social media, it would be quite challenging to conduct context-aware sentiment analysis with social media. Promising frameworks such as CoreNLP, Text Blob, and Vader have been introduced to identify sentiments in the text. However, it seems to not be adequate to contextual sentiment analysis in social media like Twitter. In this thesis, we present a contextual sentiment framework that is designed to leverage the power of the multiple models in the social context. The framework aims to classify contextual sentiment from the Twitter data as well as to discover hidden trends and topics (context) using topic modeling techniques like Latent Dirichlet Allocation (LDA). We have focused on the mismatch cases among multiple models in which different experts (models) have different opinions on social media sentiments. We have identified the five mismatch types in the social sentiment through the analysis of diverse experiments ( human machine model, and machine-machine model). We have implemented the mismatch detection among the three models (i.e., Vader, Text Blob, and CoreNLP) and automatically corrected them by applying semantic rules to sentiment models. We compared our approach against a traditional single model approach concerning a performance metric (accuracy) and Kappa (evaluating consensus among multi-models) on three benchmarks datasets and our dataset we collected from a health dieting domain. The proposed framework showed notable performance improvement in comparison with the traditional one concerning both evaluation metrics.Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future wor

Sentiment Analysis in Environmental Sustainability Field by Machine Learning

Environmental sustainability is one of the influential topics of the last decade. Most people have become more environmentally aware and educated environmentally conscious. Sustainability concerns the sustainability of natural resources and environmental protection. The four pillars of sustainability include human, social, economic and environmental. Twitter is a popular social media platform that keeps us updated on the latest news, events and trends from around the world. In 2022, the number of Twitter users in Thailand reached around 11.45 million, accounting for 16.4 % of all Thai people. Also, the fastest growing conversation in Twitter is related to the environment and sustainability. Nowadays, customers pay attention to the environmental and social impact of products they buy. Sentiment Analysis is the process of analyzing emotions or feelings by using machine learning techniques. The main objective of this exploratory study is to conduct social media opinion mining in case of the environmental sustainability field of Thai people. The paper presents the linguistic analysis of the collected data and explains discovered phenomena, including data preprocessing steps, feature extraction, and model construction to determine positive, negative and neutral sentiments. The result reveals that sentiment analysis takes place around the sustainability context mostly in positive terms to make a better understanding of the dynamics and changes in environmental sustainability society

Extracting and Grounding Context-Aware Sentiment Lexicons

Web intelligence applications track online sources with economic relevance such as customer reviews, news articles and social media postings. Automated sentiment analysis based on lexical methods or machine learning identifies the polarity of opinions expressed in these sources to assess how stakeholders perceive a topic. This paper introduces a hybrid approach that combines the throughput of lexical analysis with the flexibility of machine learning to resolve ambiguity and consider the context of sentiment terms. The context-aware method identifies ambiguous terms that vary in polarity depending on the context and stores them in contextualized sentiment lexicons. In conjunction with semantic knowledge bases, these lexicons help ground ambiguous sentiment terms to concepts that correspond to their polarity. This grounding paves the way for interlinking, extending, or even replacing contextualized sentiment lexicons with semantic knowledge bases. An extensive evaluation applies the method to user reviews across three domains (movies, products and hotels)

Towards Deep Semantic Analysis Of Hashtags

Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities ("NSA" and "Edward Snowden" in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.Comment: To Appear in 37th European Conference on Information Retrieva

Enhancing Lexical Sentiment Analysis using LASSO Style Regularization

In the current information age where expressing one’s opinions online requires but a few button presses, there is great interest in analyzing and predicting such emotional expression. Sentiment analysis is described as the study of how to quantify and predict such emotional expression by applying various analytical methods. This realm of study can broadly be separated into two domains: those which quantify sentiment using sets of features determined by humans, and approaches that utilize machine learning. An issue with the later approaches being that the features which describe sentiment within text are challenging to interpret. By combining VADER which is short for Valence Aware Dictionary for sEntiment Reasoning; a lexicon model with machine learning tools (simulated annealing) and k-fold cross validation we can improve the performance of VADER within and across context. To validate this modified VADER algorithm we contribute to the literature of sentiment analysis by sharing a dataset sourced from Steam; an online video game platform. The benefits of using Steam for training purposes is that it contains several unique properties from both social media and online web retailers such as Amazon. The results obtained from applying this modified VADER algorithm indicate that parameters need to be re-trained for each dataset/context. Furthermore that using statistical learning tools to estimate these parameters improves the performance of VADER within and across context. As an addendum we provide a general overview of the current state of sentiment analysis and apply BERT a Transformer-based neural network model to the collected Steam dataset. These results were then compared to both base VADER and modified VADER

Hybrid Words Representation for the classification of low quality text

University of Technology Sydney. Faculty of Engineering and Information Technology.Language enables humans to communicate with others. For instance, we talk, give our opinions and suggestions all using natural language; to be more precise, we use words while communicating with others. However, in today's world, we wish to communicate with computers, just like humans. It is not an easy task because human communicate in an unstructured and informal way, whereas computers need structured and clean data. So it is essential for computers to understand and classify text accurately for proper human-computer interactions. For classifying a text, the first question we must address is how to improve the low-quality text. The next immediate challenge is to have the best representation so that text can be classified accurately. The way text is organized reflects polysemy, semantic and syntactical coupling relationships which are embedded in its contents. The effective capturing of such content relationships is thereby crucial for a better understanding of text representations. This is especially challenging in the environments where the text messages are short, informal and noisy, and involves natural language ambiguities. The existing sentiment classification methods are mainly for document and clean textual data which can not capture relationship, different attributes and characteristics within tweet messages. Social media analysis, especially the analysis of tweet messages on Twitter has become increasingly relevant since the significant portion of data is ubiquitous in nature. The social media-based short text is valuable for many good reasons, explored increasingly in text analysis, social media analysis and recommendation. In the same time, there is a number of challenges that need to be addressed in this space. One of the main issues is that the traditional word embeddings are unable to capture polysemy (assigns the same representation of a word irrespective of its context and meaning) and out of vocabulary words (assigns a random representation). Furthermore, traditional word embeddings fail to capture sentiment information of words which results in similar word vector representations having the opposite polarities. Thus, ignoring polysemy within the context and sentiment polarity of words in a tweet reduces the performance for tweets classification. In order to address the above-mentioned research challenges and limitations associated with word-level representations, this thesis focuses on improving the representation of low-quality text by improving the unstructured and informal nature of tweets to utilize the information thoroughly and manages the natural language ambiguities to build a more robust sentiment classification model. As compared to previous studies, the proposed models can deal with the ubiquitous nature of the short text, polysemy, semantic and syntactical relationships within a content, thereby addressing the natural language ambiguity problems. Chapter 4 presents the effects of pre-processing techniques using two different word representation models with the machine and deep learning classifiers. Then, we present our recommended combination (approach) of different pre-processing techniques which improves the low quality, by performing sentiment-aware tokenization, correction of spelling mistakes, word segmentation and other techniques to utilize most of the information hidden in unstructured text. The experimental result shows that the proposed combination performs well as compared to other combinations. Chapter 5 presents the hybrid words representation. In this chapter, we proposed our Deep Intelligent Contextual Embedding for Twitter sentiment analysis. Proposed model addresses the natural language ambiguities and is devised to capture polysemy in context, semantics, syntax and sentiment knowledge of words. Bi-directional Long-Short Term Memory wth attention is employed to determine the sentiment. We evaluate the proposed model by performing quantitative and qualitative analysis. The experimental results show that the proposed model outperforms various word embedding models in the sentiment analysis of tweets. Above mentioned methods can be applied to any social media classification task. The performance of proposed models is compared with different models which support the effectiveness of the proposed models and bound the information loss in their generated high-quality representations

Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives

How did the popularity of the Greek Prime Minister evolve in 2015? How did the predominant sentiment about him vary during that period? Were there any controversial sub-periods? What other entities were related to him during these periods? To answer these questions, one needs to analyze archived documents and data about the query entities, such as old news articles or social media archives. In particular, user-generated content posted in social networks, like Twitter and Facebook, can be seen as a comprehensive documentation of our society, and thus meaningful analysis methods over such archived data are of immense value for sociologists, historians and other interested parties who want to study the history and evolution of entities and events. To this end, in this paper we propose an entity-centric approach to analyze social media archives and we define measures that allow studying how entities were reflected in social media in different time periods and under different aspects, like popularity, attitude, controversiality, and connectedness with other entities. A case study using a large Twitter archive of four years illustrates the insights that can be gained by such an entity-centric and multi-aspect analysis.Comment: This is a preprint of an article accepted for publication in the International Journal on Digital Libraries (2018

User sentiment detection: a YouTube use case

In this paper we propose an unsupervised lexicon-based approach to detect the sentiment polarity of user comments in YouTube. Polarity detection in social media content is challenging not only because of the existing limitations in current sentiment dictionaries but also due to the informal linguistic styles used by users. Present dictionaries fail to capture the sentiments of community-created terms. To address the challenge we adopted a data-driven approach and prepared a social media specific list of terms and phrases expressing user sentiments and opinions. Experimental evaluation shows the combinatorial approach has greater potential. Finally, we discuss many research challenges involving social media sentiment analysis