63 research outputs found
Determining wordâemotion associations from tweets by multi-label classification
The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter. We perform this expansion using multi-label classification of words and compare different wordlevel features extracted from unlabelled tweets such as unigrams, Brown clusters, POS tags, and word2vec embeddings. The results show that the expanded lexicon achieves major improvements over the original lexicon when classifying tweets into emotional categories. In contrast to previous work, our methodology does not depend on tweets annotated with emotional hashtags, thus enabling the identification of emotional words from any domainspecific collection using unlabelled tweets
Analyzing Twitter Feeds to Facilitate Crises Informatics and Disaster Response During Mass Emergencies
It is a common practice these days for general public to use various micro-blogging platforms, predominantly Twitter, to share ideas, opinions and information about things and life. Twitter is also being increasingly used as a popular source of information sharing during natural disasters and mass emergencies to update and communicate the extent of the geographic phenomena, report the affected population and casualties, request or provide volunteering services and to share the status of disaster recovery process initiated by humanitarian-aid and disaster-management organizations. Recent research in this area has affirmed the potential use of such social media data for various disaster response tasks. Even though the availability of social media data is massive, open and free, there is a significant limitation in making sense of this data because of its high volume, variety, velocity, value, variability and veracity. The current work provides a comprehensive framework of text processing and analysis performed on several thousands of tweets shared on Twitter during natural disaster events. Specifically, this work em- ploys state-of-the-art machine learning techniques from natural language processing on tweet content to process the ginormous data generated at the time of disasters. This study shall serve as a basis to provide useful actionable information to the crises management and mitigation teams in planning and preparation of effective disaster response and to facilitate the development of future automated systems for handling crises situations
Classification of socially generated medical data
The growth of online health communities, particularly those involving socially
generated content, can provide considerable value for society. Participants can
gain knowledge of medical information or interact with peers on medical forum
platforms. However, the sheer volume of information so generated â and the
consequent ânoiseâ associated with large data volumes â can create difficulties
for information consumers. We propose a solution to this problem by applying
high-level analytics to the data â primarily sentiment analysis, but also content
and topic analysis - for accurate classification. We believe that such analysis can
be of significant value to data users, such as identifying a particular aspect of an
information space, determining themes that predominate among a large dataset,
and allowing people to summarize topics within a big dataset.
In this thesis, we apply machine learning strategies to identify sentiments expressed
in online medical forums that discuss Lyme Disease. As part of this
process, we distinguish a complete and relevant set of categories that can be used
to characterize Lyme Disease discourse. We present a feature-based model that
employs supervised learning algorithms and assess the feasibility and accuracy of
this sentiment classification model. We further evaluate our model by assessing
its ability to adapt to an online medical forum discussing a disease with similar
characteristics, Lupus. The experimental results demonstrate the effectiveness of
our approach.
In many sentiment analysis applications, the labelled training datasets are
expensive to obtain, whereas unlabelled datasets are readily available. Therefore,
we present an adaptation of a well-known semi-supervised learning technique,
in which co-training is implemented by combining labelled and unlabelled data.
Our results would suggest the ability to learn even with limited labelled data. In
addition, we investigate complementary analytic techniques â content and topic
analysis â to leverage best used of the data for various consumer groups.
Within the work described in this thesis, some particular research issues are addressed,
specifically when applied to socially generated medical/health datasets:
⢠When applying binary sentiment analysis to short-form text data (e.g.
Twitter), could meta-level features improve performance of classification?
⢠When applying more complex multi-class sentiment analysis to classification
of long-form content-rich text data, would meta-level features be a useful addition to more conventional features?
⢠Can this multi-class analysis approach be generalised to other medical/health
domains?
⢠How would alternative classification strategies benefit different groups of
information consumers
Finding polarised communities and tracking information diffusion on Twitter: The Irish Abortion Referendum
The analysis of social networks enables the understanding of social
interactions, polarisation of ideas, and the spread of information and
therefore plays an important role in society. We use Twitter data - as it is a
popular venue for the expression of opinion and dissemination of information -
to identify opposing sides of a debate and, importantly, to observe how
information spreads between these groups in our current polarised climate.
To achieve this, we collected over 688,000 Tweets from the Irish Abortion
Referendum of 2018 to build a conversation network from users mentions with
sentiment-based homophily. From this network, community detection methods allow
us to isolate yes- or no-aligned supporters with high accuracy (90.9%). We
supplement this by tracking how information cascades spread via over 31,000
retweet-cascades. We found that very little information spread between
polarised communities. This provides a valuable methodology for extracting and
studying information diffusion on large networks by isolating ideologically
polarised groups and exploring the propagation of information within and
between these groups.Comment: 44 pages, 4 appendices, 18 figure
Sentiment analysis: the case of twitch chat - Mining user feedback from livestream chats
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementIn a world where users often share their thoughts and opinions through online communication
channels, applications that can tap into these channels as to extract consumer feedback have
become increasingly valuable. Traditional marketing research techniques such as interviews or
surveys offer results that pale in comparison to sentiment analysis applications that can extract
organic feedback from an extremely large selection, with very little resources and in real-time.
This thesis focuses on proposing and developing one of these tools that targets livestreams,
which have, over the years, seen a massive increase in popularity from both a user-base
standpoint as well as brand involvement. We chose the livestreaming platform âTwitchâ as the
target of research and developed a sentiment analysis model, using rule-based approaches,
capable of interpreting user chat messages and identifying whether those messages are negative,
positive or neutral. Additionally, an application was developed to better view and analyze the
results of the model. By segmenting our results by product reveal, we also exhibit how the
application allows for the extraction of various insights about the publicâs opinion of that
product
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Moment-to-moment mood change modelling in mobile mental health network
Human interests and behaviour change over time and often affected by multiple factors. In particular, human emotions, mood and its constituent processes change and interact over time. Therefore, modelling human behaviour should take into account the changes over time for customization and adaptation of systems to the usersâ specific needs. Understanding and assessing the temporal dynamics of mood are critical for modelling human behaviour for both individuals and group of people who share similar habits, life style and personal circumstances. Thus, in order to construct a personalized recommendation for a given user, it is first necessary to have some knowledge about previous user interests and behaviours. However, the challenge of obtaining large-scale data on human emotions has left the most fundamental questions on emotions less explored: How do emotions vary across individuals, evolve over time, and are connected to social ties? We address these questions using a large-scale dataset of users that contains both their usersâ interactions with momentary emotions and topical labels. Using this dataset, we identify patterns of human emotions on different levels, starting from the network level, group-level (cluster) and moving towards the user level. At the user-level, we identify how human emotions are distributed and vary over time. In particular, we model changes in mood using multi-level multimodal features including usersâ sentimental status, engagement and linguistic queries. We also utilise language models to model and understand patterns of mood change. We model the changes of usersâ mental states based on replies and responses to posts over time and predict future states. We find that the future mental states can be predicted with reasonable accuracy given usersâ historical posts, current participation features. Our findings form a step forward towards better understand the interplay between user behaviour and mood change exhibited while interacting on mental health network and providing some interpretable summaries that can be used in the future by health experts and individuals and work on possible medical interventions together with clinical experts
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