A comparative study of Bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection

Hilbert series and Hilbert depth of squarefree Veronese ideals

In this paper, we obtain explicit formulas for the Hilbert series and Hilbert depth of squarefree Veronese ideals in a standard graded polynomial ring.Comment: 7 pages, a gap in the previous version is fixe

Dietary phytoestrogens and esophageal cancer

Esophageal cancer is the eighth most common invasive cancer in the world, a cancer with an increasing incidence and male predominance, and there is a great need for potential dietary prevention. The overall aim of this thesis was to evaluate whether the dietary phytoestrogens lignans might play a protective role in the etiology of esophageal cancer, including gastroesophageal junctional adenocarcinoma. In Paper I, we examined the association between intake of dietary lignans based on a 63-item food frequency questionnaire (FFQ) and risk of esophageal cancer in a Swedish nationwide population-based case-control study conducted in 1995-1997. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Participants in the highest quartile of lignan intake compared with the lowest quartile showed a decreased risk of esophageal adenocarcinoma (OR=0.65; 95% CI: 0.38-1.12) and gastroesophageal junctional adenocarcinoma (OR=0.37; 95% CI: 0.23-0.58), while no clear associations were found for esophageal squamous-cell carcinoma. In Paper II, we validated the use of two FFQs (the 67-item FFQ-87 and the 93-item FFQ-97) for the assessment of dietary lignans compared to the serum biomarker enterolactone, the main metabolite of dietary lignans in the human body. Based on the FFQ-97, the correlation between lignan intake and serum enterolactone was significant, but the value of the correlation coefficient was small (r=0.22, p=0.01). No significant correlation was observed for the FFQ-87. In Paper III, we further evaluated the possible association between lignan intake based on the FFQ-97 and risk of esophageal and gastric adenocarcinoma using a prospective study design. Among 81,670 participants who were followed up during 1998-2009, hazard ratios (HRs) and 95% CIs were calculated. No statistically significantly decreased risk was found. Compared with the lowest quartile of lignan intake, the adjusted HRs of the highest quartile were 0.96 (95% CI: 0.46-2.00) for esophageal and gastroesophageal junctional adenocarcinoma, and 0.89 (95% CI: 0.52-1.55) for gastric adenocarcinoma. In Paper IV, we defined a dietary pattern characterized by dietary intake of lignans, quercetin and resveratrol, the three common phytochemicals with estrogenic properties, in a Swedish population-based case-control study. A decreased risk of esophageal cancer was found among individuals with a high dietary intake of these three phytochemicals. Comparing the highest quintile of food pattern score with the lowest quintile, the adjusted ORs were 0.24 (95% CI: 0.12-0.49) for esophageal adenocarcinoma, 0.31 (95% CI: 0.15-0.65) for esophageal squamous-cell carcinoma, and 0.49 (95% CI: 0.28-0.84) for gastroesophageal junctional adenocarcinoma. In conclusion, a high dietary intake of phytoestrogens, typically lignans, might decrease the risk of adenocarcinoma of esophagus and gastroesophageal junction. The FFQ-97 can be used to assess lignan exposure, and a dietary pattern characterized by a high dietary intake of lignans, quercetin, and resveratrol might prevent esophageal cancer

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning

The Enlightenment of Affective Filter Hypothesis and Risk-Taking on English Learning

Affective filter hypothesis reveals that learners with different emotional learning attitudes have different filter capability for language learning input. Learners with positive emotional attitude have a low filter of language learning input, while learners with negative emotional attitudes have a high filter of language learning input. Risk-taking means that the learner dares to take risks. He is not afraid of making mistakes and the unknown situation. The risk taker will seize every opportunity to use learned knowledge into practice, which is a positive emotional attitude. In the meantime, the adventurous students’ affective filter is relatively low. In actual classroom, teachers usually pay little attention to the emotional state of students, and teachers rarely realize the impact of motivation, self-confidence, anxiety, and risk taking on students’ English learning. More attention is still paid to the training of students’ basic skills. Therefore, this paper first explains the connotation of the affective filter hypothesis and risk-taking. After analyzing and explaining the actual teaching situation and the current situation of students, four suggestions are put forward from the perspectives of teachers and students. Among them, the first three suggestions are for teachers, and the last one is for students. The first suggestion is about teaching methods. Teachers should get rid of the obstacles of traditional teaching methods, use multimedia and other technology to assist teaching and give students more opportunities to speak in class. The second suggestion is about teaching atmosphere. Creating a relaxation and pleasant classroom environment is conducive to reducing students’ anxiety and good for students to take risks. The third suggestion is about self-confidence. In order to build students’ confidence in English learning, teachers should encourage students, discover the highlights of each student and praise students when they make progress. The fourth suggestion is about the significance of affective factors. Students should recognize the role of emotional factors in English learning and adopt effective methods to self-regulate

On Choosing Initial Values of Iteratively Reweighted ℓ1\ell_1 Algorithms for the Piece-wise Exponential Penalty

Computing the proximal operator of the sparsity-promoting piece-wise exponential (PiE) penalty $1-e^{-|x|/\sigma}$ with a given shape parameter $\sigma>0$, which is treated as a popular nonconvex surrogate of $\ell_0$-norm, is fundamental in feature selection via support vector machines, image reconstruction, zero-one programming problems, compressed sensing, etc. Due to the nonconvexity of PiE, for a long time, its proximal operator is frequently evaluated via an iteratively reweighted $\ell_1$ algorithm, which substitutes PiE with its first-order approximation, however, the obtained solutions only are the critical point. Based on the exact characterization of the proximal operator of PiE, we explore how the iteratively reweighted $\ell_1$ solution deviates from the true proximal operator in certain regions, which can be explicitly identified in terms of $\sigma$, the initial value and the regularization parameter in the definition of the proximal operator. Moreover, the initial value can be adaptively and simply chosen to ensure that the iteratively reweighted $\ell_1$ solution belongs to the proximal operator of PiE

TDAM: a topic-dependent attention model for sentiment analysis

We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words' local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users' reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training