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On stopwords, filtering and data sparsity for sentiment analysis of Twitter
Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier’s feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space
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
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