220,548 research outputs found
A context based model for sentiment analysis in twitter for the italian language
Studi recenti per la Sentiment
Analysis in Twitter hanno tentato di creare
modelli per caratterizzare la polarit´a di
un tweet osservando ciascun messaggio
in isolamento. In realt`a, i tweet fanno
parte di conversazioni, la cui natura pu`o
essere sfruttata per migliorare la qualit`a
dell’analisi da parte di sistemi automatici.
In (Vanzo et al., 2014) `e stato proposto un
modello basato sulla classificazione di sequenze
per la caratterizzazione della polarit`
a dei tweet, che sfrutta il contesto in
cui il messaggio `e immerso. In questo lavoro,
si vuole verificare l’applicabilit`a di
tale metodologia anche per la lingua Italiana.Recent works on Sentiment
Analysis over Twitter leverage the idea
that the sentiment depends on a single
incoming tweet. However, tweets are
plunged into streams of posts, thus making
available a wider context. The contribution
of this information has been recently
investigated for the English language by
modeling the polarity detection as a sequential
classification task over streams of
tweets (Vanzo et al., 2014). Here, we want
to verify the applicability of this method
even for a morphological richer language,
i.e. Italian
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Perception and expression of emotion are key factors to the success of
dialogue systems or conversational agents. However, this problem has not been
studied in large-scale conversation generation so far. In this paper, we
propose Emotional Chatting Machine (ECM) that can generate appropriate
responses not only in content (relevant and grammatical) but also in emotion
(emotionally consistent). To the best of our knowledge, this is the first work
that addresses the emotion factor in large-scale conversation generation. ECM
addresses the factor using three new mechanisms that respectively (1) models
the high-level abstraction of emotion expressions by embedding emotion
categories, (2) captures the change of implicit internal emotion states, and
(3) uses explicit emotion expressions with an external emotion vocabulary.
Experiments show that the proposed model can generate responses appropriate not
only in content but also in emotion.Comment: Accepted in AAAI 201
Characterizing Pedophile Conversations on the Internet using Online Grooming
Cyber-crime targeting children such as online pedophile activity are a major
and a growing concern to society. A deep understanding of predatory chat
conversations on the Internet has implications in designing effective solutions
to automatically identify malicious conversations from regular conversations.
We believe that a deeper understanding of the pedophile conversation can result
in more sophisticated and robust surveillance systems than majority of the
current systems relying only on shallow processing such as simple word-counting
or key-word spotting.
In this paper, we study pedophile conversations from the perspective of
online grooming theory and perform a series of linguistic-based empirical
analysis on several pedophile chat conversations to gain useful insights and
patterns. We manually annotated 75 pedophile chat conversations with six stages
of online grooming and test several hypothesis on it. The results of our
experiments reveal that relationship forming is the most dominant online
grooming stage in contrast to the sexual stage. We use a widely used
word-counting program (LIWC) to create psycho-linguistic profiles for each of
the six online grooming stages to discover interesting textual patterns useful
to improve our understanding of the online pedophile phenomenon. Furthermore,
we present empirical results that throw light on various aspects of a pedophile
conversation such as probability of state transitions from one stage to
another, distribution of a pedophile chat conversation across various online
grooming stages and correlations between pre-defined word categories and online
grooming stages
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E-word of mouth: building sustainable and trustworthy relationships with customers in a highly regulated on-line environment
With the increasing popularity of social media, consumers now turn to different online
discussion forums, consumer review sites, weblogs, social network sites and so on to seek
product information and share their own experiences (Cheung and Thadani, 2010; Davies,
2008). Consequently, companies today face an increasingly difficult challenge: how to
communicate with consumers online in a way that encourages trust and engagement? What
may make things even more complicated is that many companies are now operating in a
highly regulated environment, with the healthcare industry a typical example of this (Choi
and Lee, 2007; Huh and Langteau, 2007; Nielson, 2008; von Knoop et al., 2003). Thus,
pharmaceutical marketers and brand managers must understand how to communicate
effectively in a highly regulated online environment.
This short report aims to help such companies to build a sustainable and trustworthy
relationship with their customers online. To do so, the report considers the subject from the
perspective of a pharmaceutical company. It will first discuss the current regulations around
the healthcare industry, highlighting the constraints pharmaceutical marketers need to face.
Then, it will review current literature discussing healthcare consumers’ online behaviour. In
particular, it will focus on consumers’ negative comments and their possible impact on the
business. Finally, the report concludes with some suggestions about how to cope with
negative comments online and build a reliable relationship with customers
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
Delaying dispreferred responses in English: From a Japanese perspective
This article employs conversation analysis to explore the interpenetration of grammar and preference organization in English conversation in comparison with a previous study for Japanese. Whereas varying the word order of major syntactic elements is a vital grammatical resource in Japanese for accomplishing the potentially universal task of delaying dispreferred responses to a range of first actions, it is found to have limited utility in English. A search for alternative operations and devices that conversationalists deploy for this objective in English points to several grammatical constructions that can be tailored to maximize the delay of dispreferred responses. These include the fronting of relatively mobile, syntactically ?non-obligatory? elements of clause structure and the employment of various copular constructions. A close interdependence is observed between the rudimentary grammatical resources available in the two languages and the types of operations that are respectively enlisted for the implementation of the organization of preference
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
Conversational participants tend to immediately and unconsciously adapt to
each other's language styles: a speaker will even adjust the number of articles
and other function words in their next utterance in response to the number in
their partner's immediately preceding utterance. This striking level of
coordination is thought to have arisen as a way to achieve social goals, such
as gaining approval or emphasizing difference in status. But has the adaptation
mechanism become so deeply embedded in the language-generation process as to
become a reflex? We argue that fictional dialogs offer a way to study this
question, since authors create the conversations but don't receive the social
benefits (rather, the imagined characters do). Indeed, we find significant
coordination across many families of function words in our large movie-script
corpus. We also report suggestive preliminary findings on the effects of gender
and other features; e.g., surprisingly, for articles, on average, characters
adapt more to females than to males.Comment: data available at http://www.cs.cornell.edu/~cristian/movie
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.Comment: ACL 201
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