Abstract. In this paper we consider the detection of opinion spam as a stylistic classification task because, given a particular domain, the deceptive and truthful opinions are similar in content but differ in the way opinions are written (style). Particularly, we propose using character ngrams as features since they have shown to capture lexical content as well as stylistic information. We evaluated our approach on a standard corpus composed of 1600 hotel reviews, considering positive and negative reviews. We compared the results obtained with character n-grams against the ones with word n-grams. Moreover, we evaluated the effectiveness of character n-grams decreasing the training set size in order to simulate real training conditions. The results obtained show that character n-grams are good features for the detection of opinion spam; they seem to be able to capture better than word n-grams the content of deceptive opinions and the writing style of the deceiver. In particular, results show an improvement of 2.3% and 2.1% over the word-based representations in the detection of positive and negative deceptive opinions respectively. Furthermore, character n-grams allow to obtain a good performance also with a very small training corpus. Using only 25% of the training set, a Naïve Bayes classifier showed F1 values up to 0.80 for both opinion polarities.

We review fiber-based multimode interference (MMI) devices with a particular focus on optical fiber-based sensing applications. The present review complements a recently published, extensive review where the sensing of conventional physical variables such as refractive index, temperature, displacement, and strain was covered. This review focuses on MMI fiber sensors for nonconventional physical variables, including mechanical, electromagnetic, chemical, and optical, covering around fifteen years of work in the field. Finally, by the end of this paper, we also review some new trends of MMI-based schemes based on polymer fibers, for wavelength-locking applications, for retrieving the thermo-optic coefficient of liquid samples, and for measuring the dynamics of complex fluids.

Nowadays a large number of opinion reviews are posted on the Web. Such reviews are a very important source of information for customers and companies. The former rely more than ever on online reviews to make their purchase decisions, and the latter to respond promptly to their clients' expectations. Unfortunately, due to the business that is behind, there is an increasing number of deceptive opinions, that is, fictitious opinions that have been deliberately written to sound authentic, in order to deceive the consumers promoting a low quality product (positive deceptive opinions) or criticizing a potentially good quality one (negative deceptive opinions). In this paper we focus on the detection of both types of deceptive opinions, positive and negative. Due to the scarcity of examples of deceptive opinions, we propose to approach the problem of the detection of deceptive opinions employing PU-learning. PU-learning is a semi-supervised technique for building a binary classifier on the basis of positive (i.e., deceptive opinions) and unlabeled examples only. Concretely, we propose a novel method that with respect to its original version is much more conservative at the moment of selecting the negative examples (i.e., not deceptive opinions) from the unlabeled ones. The obtained results show that the proposed PU-learning method consistently outperformed the original PU-learning approach.

No abstract

A simple and compact fiber optic sensor based on a two-core fiber is demonstrated for high-performance measurements of refractive indices (RI) of liquids. In order to demonstrate the suitability of the proposed sensor to perform high-sensitivity sensing in a variety of applications, the sensor has been used to measure the RI of binary liquid mixtures. Such measurements can accurately determine the salinity of salt water solutions, and detect the water content of adulterated alcoholic beverages. The largest sensitivity of the RI sensor that has been experimentally demonstrated is 3,119 nm per Refractive Index Units (RIU) for the RI range from 1.3160 to 1.3943. On the other hand, our results suggest that the sensitivity can be enhanced up to 3485.67 nm/RIU approximately for the same RI range.