In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understand the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that (1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa), and (2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from TripAdvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well-established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that—on a scale of five stars—those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews—on an initial very large dataset—that may allow both consumers and providers to focus directly on the review subset featuring a text score disagreement,which conveniently convey to the user a summary of positive and negative features of the review target.
Concerning subjectivity in texts, i.e., those expressions representing opinions and speculations, work in @cite_5 is one of the first studies to perform subjectivity analysis, to identify subjective sentences and their features. In the specific field of polarity detection applied to product reviews, work in @cite_36 assigns a numerical score to a textual description exploiting the SentiWordNet lexicon: the task is especially useful when a reviews platform only allows to leave a text as a review, without an associated numerical score. A more recent work in @cite_7 considers analogous topics. Work in @cite_43 proposes an unsupervised approach that involves the extraction of terms and slangs polarities from three sentiment lexicons and the aggregation of such scores to predict the overall sentiment of a tweet . @cite_37 @cite_12 @cite_35 , the authors consider the contextual polarity of a word, i.e., the polarity acquired by the word contextually to the sentence in which it appears. For a survey of sentiment analysis algorithms and applications, the interested reader can refer to @cite_38 . For the specific scenario of polarity evaluation and sentiment analysis in specific social networks, the interested reader can refer to the series of work in @cite_33 @cite_40 , inherent to Twitter.
@cite_35 @cite_37 @cite_38 @cite_33 @cite_7 @cite_36 @cite_43 @cite_40 @cite_5 @cite_12
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We propose a supervised model for expanding an opinion lexicon for Twitter.We combine automatically annotated tweets with existing hand-made opinion lexicons.We use POS tags and associations between words and sentiment as word-level features.Expanded words are mapped to a positive, negative, and neutral distribution.We outperform the performance obtained by using PMI semantic orientation alone. Opinion lexicons, which are lists of terms labeled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media.In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet.To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modeled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labeled by a seed lexicon formed by combining multiple hand-annotated lexicons.Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve significant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments. The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people's sentiments toward different topics. In most sentiment analysis applications, the sentiment lexicon plays a central role. However, it is well known that there is no universally optimal sentiment lexicon since the polarity of words is sensitive to the topic domain. Even worse, in the same domain the same word may indicate different polarities with respect to different aspects. For example, in a laptop review, "large" is negative for the battery aspect while being positive for the screen aspect. In this paper, we focus on the problem of learning a sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection. We propose a novel optimization framework that provides a unified and principled way to combine different sources of information for learning such a context-dependent sentiment lexicon. Experiments on two data sets (hotel reviews and customer feedback surveys on printers) show that our approach can not only identify new sentiment words specific to the given domain but also determine the different polarities of a word depending on the aspect in context. In further quantitative evaluation, our method is proved to be effective in constructing a high quality lexicon by comparing with a human annotated gold standard. In addition, using the learned context-dependent sentiment lexicon improved the accuracy in an aspect-level sentiment classification task. Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain. Deriving prior polarity lexica for sentiment analysis – where positive or negative scores are associated with words out of context – is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called ‘ensemble method’). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords , a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica.