48,014 research outputs found
Attitudes expressed in online comments about environmental factors in the tourism sector: an exploratory study
The object of this exploratory study is to identify the positive, neutral and negative
environment factors that affect users who visit Spanish hotels in order to help the hotel managers
decide how to improve the quality of the services provided. To carry out the research a Sentiment
Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings
shown and then a textual analysis was used to identify the key environment factors in these feelings
using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results
of the exploratory study present the key environment factors that affect the users experience when
visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of
rural areas respecting the local environment and nature, or respecting air quality in the areas where
hotels have facilities and offer services. The conclusions of the research can help hotels improve their
services and the impact on the environment, as well as improving the visitors experience based on
the positive, neutral and negative environment factors which the visitors themselves identified
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analysis to extract aspects from textual opinions and then build an
abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
graphs to evaluate the importance of aspects and to filter out unimportant ones
from the summary. Additionally, the paper presents a prototype solution of data
flow with interesting and valuable results. The proposed method's results
proved the high accuracy of aspect detection when applied to the gold standard
dataset
Determining the polarity of postings for discussion search
When performing discussion search it might be desirable to consider non-topical measures like the number of positive and negative replies to a posting, for instance as one possible indicator for the trustworthiness of a comment. Systems like POLAR are able to integrate such values into the retrieval function. To automatically detect the polarity of postings, they need to be classified into positive and negative ones w.r.t.\ the comment or document they are annotating. We present a machine learning approach for polarity detection which is based on Support Vector Machines. We discuss and identify appropriate term and context features. Experiments with ZDNet News show that an accuracy of around 79\%-80\% can be achieved for automatically classifying comments according to their polarity
Learning sentiment from studentsâ feedback for real-time interventions in classrooms
Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded
- âŠ