Unstructured User-generated Content Research Articles

Analyzing the effect of user‐generated content on studio performance: A combined approach

AbstractThis research explores how user‐generated content (UGC) influences the performance of studios. Our dataset included both structured and unstructured UGC, analyzed through a hybrid of statistical techniques and machine learning algorithms. The findings revealed a positive relationship between UGC and both box office revenues and stock market performance. Additionally, classical machine learning methods demonstrated exceptional capabilities in accurately classifying the data. The insights from this study offer valuable marketing strategies for studio marketing managers and studio executives in leveraging social networks effectively.

A competitive intelligence acquisition framework for mining user perception from user generated content

Competitive intelligence is an important basis for companies in the process of developing competitive strategies. However, competitive intelligence acquisition methods such as surveys and expert ratings are not able to achieve rapid responses to user perceptions. In addition, the huge amount of unstructured user-generated content makes it more difficult to analyze user perceptions. In this paper, we propose a competitive intelligence mining framework for acquiring user perceptions from user-generated content. The framework covers multiple aspects of competitive intelligence acquisition, mining, analysis, and decision-making. A unified text processing model (WS-TCM) can automatically filter the content irrelevant to competitive intelligence and quickly extract fine-grained user perceptions of competitive attributes from the huge amount of user-generated content. In addition, the quantile-based intelligence mapping method (QB-IM) determines the competitive landscape of an enterprise based on fine-grained user perceptions and provides help for managers’ strategic decisions. In the case study, the method proposed in this paper has significantly improved compared with the baseline model in the stages of competitive intelligence identification, competitive attribute identification, and user perception identification. Especially, the accuracy improvement in identifying user dissatisfaction perception is more obvious. Meanwhile, our model shows strong robustness. In the competitive analysis stage, the results of the framework are consistent with those obtained by market analysts through surveys. This study provides a new approach to competitive intelligence research and fine-grained user perception mining, which improves the efficiency of competitive intelligence acquisition for companies while also improving the reliability, accuracy, and usefulness of competitive intelligence.

From the black box to the glass box: Using unsupervised and supervised learning processes to predict user engagement for the airline companies

Firms collect an enormous amount of user generated content (UGC), such as social media posts, to analyze consumers’ unfiltered opinions regarding brands and firms. A challenge in analyzing unstructured UGC is the lack of analytic frame. By adopting both unsupervised and supervised learning processes for using artificial intelligence (AI), we collected 680,410, tweets related to airline companies (United Airlines, Delta Airlines, Southwest Airlines, Alaska Airlines, and Hawaiian Airlines) and analyzed 4961 retweets to predict user engagement levels on Twitter. Rooted in the electronic word-of-mouth (eWOM) perspective, the results of this study indicated that consumer sentiment was positive for United Airlines, Delta Airlines, and Alaska Airlines, whereas it was negative for Southwest Airlines and Hawaiian Airlines. We also examined the effects of word count, gaps between the tweet generated date and the retweeted date, the number of the hashtag(s), and extracted topics on predicting the level of user engagement. Ultimately, this study provided a detailed guide to mangers on how to use an unstructured data analysis procedure incorporating both supervised and unsupervised learning processes.

AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information

Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain.

Confirmatory aspect-level opinion mining processes for tourism and hospitality research: a proposal of DiSSBUS

ABSTRACT We proposed a new rule-based text analysis method to effectively summarize and transform unstructured user-generated content (online customer reviews) into an analysable form for tourism and hospitality research. To differentiate this method, we developed the Disintegrating, Summarizing, Straining, Bagging, Upcycling, and Scoring – DiSSBUS – algorithm which can address the following problems in previous approaches: (1) false identification of irrelevant aspect terms, (2) improper handling of multiple aspects and sentiments within a text unit, and (3) data sparsity. The algorithm’s distinctive advantage is to decompose a single review into a set of bi-terms related to the aspects that are pre-specified based on domain knowledge. Therefore, this algorithm can identify customer opinions on specific aspects, which allows to extract variables of interest from online reviews. To evaluate the performance of our confirmatory aspect-level opinion-mining algorithm, we applied it to customer reviews on restaurants in Hawaii. The findings from the empirical test validated its effectiveness.

Let Photos Speak: The Effect of User-Generated Visual Content on Hotel Review Helpfulness

A picture is worth a thousand words. User-generated photos (UGPs) are increasingly accompanying online reviews of hotels. This article draws on media richness theory to estimate the effects of UGPs on hotel reviews’ helpfulness. Based on a sample of 1,159,590 valid reviews with 464,316 photos, we utilized an integrated analytical model incorporating both econometric analyses and image-processing techniques. The results show that reviews accompanied by UGPs are generally rated as more helpful than those with textual content only. Furthermore, photos showing guestroom objects were rated as more helpful than those showing food & beverages. Finally, the positive effects of UGPs on review helpfulness were especially prominent for hotels with lower prices and negative reviews. This study adds new insights to the online review literature and advances the methodological approach in analyzing unstructured user-generated content. This study provides important implications for hotel managers and online booking platforms regarding UGP management.

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing.

User-generated content (UGC) is an important data source for tourism GIScience research. However, no effective approach exists for identifying hidden spatiotemporal patterns within multi-scale unstructured UGC. Therefore, we developed an algorithm to measure the tourist destination popularity (TDP) based on a multi-spatiotemporal text granular computing model, called TDPMTGC. To accurately granulate the spatial and temporal information of tourism text, tourism text data granules are used to represent landscape objects. These granules are unified objects that possess multiple attributes, such as spatial and temporal dimensions. The multi-spatiotemporal scales are characterized by the multi-hierarchical structure of granular computing, and transformations of granular layers and data granule size are achieved by scale selection in the spatial and temporal dimensions. Therefore, all scales between the spatial and temporal dimension are related, which allows for the comparability of the data granules of all spatial-spatial, temporal-temporal and spatial-temporal layers. This approach achieves a quantitative description and comparison of the popularity value of granules between adjacent scales and cross-scales. Therefore, the TDP with multi-spatiotemporal scales can be deduced and calculated in a systematic framework. We first introduce the conceptual framework of TDPMTGC to construct a quantitative measurement model of TDP at multi-spatiotemporal scales. Then, we present a dataset construction approach to support multi-spatiotemporal scale granular reorganization. Finally, TDPMTGC is derived to describe both the TDP at a single spatial or temporal scale and the patterns and processes of the TDP at multi-spatiotemporal scales. A case study from Jiuzhaigou shows that the TDP derived using TDPMTGC is consistent with the conclusions of existing studies. More importantly, TDPMTGC provides additional detailed characteristics, such as the contributions of different scenic spots in a tourist route or scenic area, the monthly anomalies and daily contributions of TDP in a specific year, the distinct weakening of tourist route scale in tourist cognition, and the daily variations of TDP during in-season and off-season times. This is the first time that a granular computing model has been introduced to tourism GIScience that provides a feasible scheme for reorganizing large-scale unstructured text and constructing public spatiotemporal UGC tourism datasets. TDPMTGC constitutes a new approach for exploring tourist behaviors and the driving mechanisms of tourism patterns and processes.

Neural attention with character embeddings for hay fever detection from twitter

The paper aims to leverage the highly unstructured user-generated content in the context of pollen allergy surveillance using neural networks with character embeddings and the attention mechanism. Currently, there is no accurate representation of hay fever prevalence, particularly in real-time scenarios. Social media serves as an alternative to extract knowledge about the condition, which is valuable for allergy sufferers, general practitioners, and policy makers. Despite tremendous potential offered, conventional natural language processing methods prove limited when exposed to the challenging nature of user-generated content. As a result, the detection of actual hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major problem. We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data. Improvement in prediction is achieved due to the character-level semantics introduced, which effectively addresses the out-of-vocabulary problem in our dataset where the rate is approximately 9%. Overall, the study is a step forward towards improved real-time pollen allergy surveillance from social media with state-of-art technology.

Examining the impacts of mental workload and task-technology fit on user acceptance of the social media search system

Information overload has been an important issue in today’s big data era where a huge amount of unstructured user-generated content in different languages is being created on the Web in every minute. Social media search systems could help with it by effectively and efficiently collecting, storing, organizing and presenting user-generated content across the Web in an organized and timely manner. However, little research has been done to examine factors that could influence user acceptance on this new type of systems. To address it, this study develops a research model by integrating Mental Workload (MWL), Task-Technology Fit (TTF), and the unified theory of acceptance and use of technology (UTAUT). The model is tested on a security-related social media search system. The results indicate that both MWL and TTF can significantly influence user acceptance. We also operationalize the multi-dimensional latent construct of MWL by developing survey-based measurement items for different dimensions.

Identifying domain relevant user generated content through noise reduction: a test in a Chinese stock discussion forum

PurposeGetting high-quality data by removing the noisy data from the user-generated content (UGC) is the first step toward data mining and effective decision-making based on ubiquitous and unstructured social media data. This paper aims to design a framework for revoking noisy data from UGC.Design/methodology/approachIn this paper, the authors consider a classification-based framework to remove the noise from the unstructured UGC in social media community. They treat the noise as the concerned topic non-relevant messages and apply a text classification-based approach to remove the noise. They introduce a domain lexicon to help identify the concerned topic from noise and compare the performance of several classification algorithms combined with different feature selection methods.FindingsExperimental results based on a Chinese stock forum show that 84.9 per cent of all the noise data from the UGC could be removed with little valuable information loss. The support vector machines classifier combined with information gain feature extraction model is the best choice for this system. With longer messages getting better classification performance, it has been found that the length of messages affects the system performance.Originality/valueThe proposed method could be used for preprocessing in text mining and new knowledge discovery from the big data.

Sentimental interplay between structured and unstructured user-generated contents

Purpose– User-generated content (UGC), i.e. the feedback from consumers in the electronic market, including structured and unstructured types, has become increasingly important in improving online businesses. However, the ambiguity and heterogeneity, and even the conflict between the two types of UGC, require a better understanding from the perspective of human cognitive psychology. By using online feedback on hotel services, the purpose of this paper is to explore the effects of satisfaction level, opinion dispersion and cultural context background on the interrelationship between structured and unstructured UGC.Design/methodology/approach– Natural language processing techniques – specifically, topic classification and sentiment analysis on the sentence level – are adopted to retrieve consumer sentiment polarity on five attributes relative to itemized ratings. Canonical correlation analyses are conducted to empirically validate the interplay between structured and unstructured UGC among different populations segmented by the mean-variance approach.Findings– The variety of cognitions displayed by individuals affects the general significant interrelationship between structured and unstructured UGC. Extremely dissatisfied consumers or those with heterogeneous opinions tend to have a closer interconnection, and the interaction between valence and dispersion further strengthens or loosens the relationship. The satisfied or neutral consumers tend to show confounding sentiment signals in relation to the two different UGC. Chinese consumers behave differently from non-Chinese consumers, resulting in a relatively looser interplay.Practical implications– By identifying consistent opinion providers and promoting more valuable UGC, UGC platforms can raise the quality of information generated. Hotels will then be able to enhance their services through the strategic use of UGC by analyzing reviews with dispersed low-itemized rating and by addressing the differences exhibited by non-Chinese customers. This analytical method can also help to create richly structured sentiment information from unstructured UGC.Originality/value– This paper investigates the variety of cognitive behaviors in the process when UGC are contributed by online reviewers, focussing on the consistency between structured and unstructured UGC. The study helps researchers understanding emotion recognition and affective computing in social media analytics, which is achieved by exploring the variety of UGC information and its relationship to the contributors’ cognitions. The analytical framework adopted also improves the prior techniques.

Harvesting and Summarizing User-Generated Content for Advanced Speech-Based HCI

There are many Web-based platforms where people could share user-generated content such as reviews, posts, blogs, and tweets. However, online communities and social networks are expanding so rapidly that it is impossible for people to digest all the information. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest, assists the user via speech-navigated interactions, harvests and summarizes the Web data as well as presenting it in a natural way. This work, therefore, aims to conduct research on a universal framework for developing a speech-based interface that can aggregate user-generated content and present the summarized information via speech-based human-computer interactions. The challenge is two-fold. Firstly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Secondly, how to develop a dialogue modeling mechanism to present the highlighted information via natural language? This work explores plausible approaches to tackling these challenges. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated content. We will also explore sentiment-involved opinion summarization and dialogue modeling approaches for aggregated information representation. A restaurant-domain prototype system has been implemented for demonstration.

Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data

Recently, many local review websites such as Yelp are emerging, which have greatly facilitated people's daily life such as cuisine hunting. However they failed to meet travelers' demands because travelers are more concerned about a city's local specialties instead of the city's high ranked restaurants. To solve this problem, this paper presents a local specialty mining algorithm, which utilizes both the structured data from local review websites and the unstructured user-generated content (UGC) from community Q&A websites, and travelogues. The proposed algorithm extracts dish names from local review data to build a document for each city, and appliestfidfweighting algorithm on these documents to rank dishes. Dish-city correlations are calculated from unstructured UGC, and combined with thetfidfranking score to discover local specialties. Finally, duplicates in the local specialty mining results are merged. A recommendation service is built to present local specialties to travelers, along with specialties' associated restaurants, Q&A threads, and travelogues. Experiments on a large data set show that the proposed algorithm can achieve a good performance, and compared to using local review data alone, leveraging unstructured UGC can boost the mining performance a lot, especially in large cities.

The influence of personalization on tag query length in social media search

Social content systems contain enormous collections of unstructured user-generated content, annotated by the collaborative effort of regular Internet users. Tag-clouds have become popular interfaces that allow users to query the database of these systems by clicking relevant terms. However, these single click queries are often not expressive enough to effectively retrieve the desired content. Users have to use multiple clicks or type longer queries to satisfy their information need. To enhance the predicted content ranking we use a random walk model that effectively integrates the user’s preference and semantically related query terms. We use the collaborative annotations from a popular on-line book catalog to create a social annotation graph and study the effect of personalization and smoothing for increasing query lengths. We show that personalization and smoothing allow the user to find equally relevant content with fewer query terms compared to a frequency based content ranking with TF–IDF weighing. As expected, we see that the influence of the random walk model disappears if users type more detailed queries. Finally, we discuss the observations with respect to synonyms and homographs which are well known to hamper the performance of information retrieval systems.