6,836 research outputs found
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
Online Reviews as a Measure of Service Quality
The proliferation of socialized data offers an unprecedented opportunity for designing customer service measurement systems. In this paper we address the problem of adequately measuring service quality using socialized data. The theoretical basis for the study is the widely used SERVQUAL model. The analysis is based on a database of online reviews generated on the website of the leading price comparison engine in Italy. We use a weakly supervised topic model to extract relevant dimensions of service quality from the user-generated content. Despite its exploratory nature the study offers two contributions. First, it demonstrated that socialized textual data, not just quantitative ratings, provide a wealth of customer service information that can be used to measure the quality offered by service providers. Second, it shows that the distribution of topics in opinions differs significantly between positive and negative reviews. Specifically, we find that concerns about merchant responsiveness dominate negative reviews
Computing the Affective-Aesthetic Potential of Literary Texts
In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results
Understanding relationship quality in hospitality services : A study based on text analytics and partial least squares
Purpose – The purpose of this paper is to analyze the occurrence of terms to identify the relevant topics and
then to investigate the area (based on topics) of hospitality services that is highly associated with relationship
quality. This research represents an opportunity to fill the gap in the current literature, and clarify the
understanding of guests’ affective states by evaluating all aspects of their relationship with a hotel.
Design/methodology/approach – This research focuses on natural opinions upon which machine-learning
algorithms can be executed: text summarization, sentiment analysis and latent Dirichlet allocation (LDA).
Our data set contains 47,172 reviews of 33 hotels located in Las Vegas, and registered with Yelp. A component-
based structural equation modeling (partial least squares (PLS)) is applied, with a dual – exploratory and
predictive – purpose.
Findings – To maintain a truly loyal relationship and to achieve competitive success, hospitality managers
must take into account both tangible and intangible features when allocating their marketing efforts to
satisfaction-, trust- and commitment-based cues. On the other hand, the application of the PLS predict
algorithm demonstrates the predictive performance (out-of-sample prediction) of our model that supports its
ability to predict new and accurate values for individual cases when further samples are added.
Originality/value – LDA and PLS produce relevant informative summaries of corpora, and confirm and
address more specifically the results of the previous literature concerning relationship quality. Our results
are more reliable and accurate (providing insights not indicated in guests’ ratings into how hotels can
improve their services) than prior statistical results based on limited sample data and on numerical
satisfaction ratings alone
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