Search engine For Twitter sentiment analysis

textThe purpose of sentiment analysis is to determine the attitude of a writer or a speaker with respect to some topic or his feeling in a document. Thanks to the rise of social media, nowadays there are numerous data generated by users. Mining and categorizing these data will not only bring profits for companies, but also benefit the nation. Sentiment analysis not only enables business decision makers to better understand customers' behaviors, but also allows customers to know how the public feel about a product before purchasing. On the other hand, the aggregation of emotions will effectively measure the public response toward an event or news. For example, the level of distress and sadness will increase significantly after terror attacks or natural disaster. In our project, we are going to build a search engine that allows users to check the sentiment of his query. Some of previous researches on classifying sentiment of messages on micro-blogging services like Twitter have tried to solve this problem but they have ignored neutral tweets, which will result in problematic results (12). Our sentiment analysis will also be based on tweets collected from twitter, since twitter can offer sufficient and real-time corpora for analysis. We will preprocess each tweet in the training set and label it as positive, negative or neutral. As we use words in the tweet as the feature for our model, different features will be used. We will show that accuracy achieved by different machine learning algorithms (Naïve Bayes, Maximum Entropy) can be improved with a feature vector obtained by using bigrams (5). In our practice, we find that Naive Bayes has better performance than Maximum Entropy.Statistic

Non-linear Learning for Statistical Machine Translation

Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data. In this paper, we propose a non-linear modeling for the quality of translation hypotheses based on neural networks, which allows more complex interaction between features. A learning framework is presented for training the non-linear models. We also discuss possible heuristics in designing the network structure which may improve the non-linear learning performance. Experimental results show that with the basic features of a hierarchical phrase-based machine translation system, our method produce translations that are better than a linear model.Comment: submitted to a conferenc

Highly Sensitive and Selective Gas Sensors Based on Vertically Aligned Metal Oxide Nanowire Arrays

Mimicking the biological olfactory systems that consist of olfactory receptor arrays with large surface area and massively-diversified chemical reactivity, three dimensional (3D) metal oxide nanowire arrays were used as the active materials for gas detection. Metal oxide nanowire arrays share similar 3D structures as the array of mammal\u27s olfactory receptors and the chemical reactivity of nanowire array can be modified by surface coatings. In this dissertation, two standalone gas sensors based on metal oxide nanowire arrays prepared by microfabrication and in-situ micromanipulation, respectively, have been demonstrated. The sensors based on WO3 nanowire arrays can detect 50 ppb NO2 with a fast response; well-aligned CuO nanowire array present a new detection mechanism, which can identify H2S at a concentration of 500 ppb. To expand the material library of 3D metal oxide nanowire arrays for gas sensing, a general route to polycrystalline metal oxide nanowire array has been introduced by using ZnO nanowire arrays as structural templates. The effectiveness of this method for high performance gas sensing was first investigated by single-nanowire devices. The polycrystalline metal oxide coatings showed high performance for gas detection and their sensitivity can be further enhanced by catalytic noble metal decorations. To form electronic nose systems, different metal oxide coatings and catalytic decorations were employed to diversify the chemical reactivity of the sensors. The systems can detect low concentrated H2S and NO2 at room temperature down to part-per-billion level. The system with different catalytic metal coatings is also capable of discriminiating five different gases (H2S, NO2, NH3, H2 and CO)

Simulations of Bosonic Dark Matter

Dark matter is a hypothetical form of matter, which is thought to make up nearly $27\%$ of the contents in our Universe. An increasingly popular idea is that the dark matter could be composed of light (pseudo-)scalar particles with large occupation number so that they can be described by a classical scalar field $\phi$, with the mass $\approx 10^{-22}$eV. As the finite energy ground state solutions for such a field, boson stars are a good subject in the study of dark matter. In this dissertation, the primary focus is on boson stars and their surrounding miniclusters. Firstly, using my new algorithms employing the Pseudo-Spectral method, I simulate the collision of two boson stars, and find the interference pattern when two boson stars overlap. The relationship between boson stars and the surrounding miniclusters are also introduced. Secondly, I study the formation and growth of boson stars in their surrounding miniclusters by gravitational condensation using the numerical method developed. Fully dynamical attractive and repulsive self-interactions are considered for the first time. In the case of pure gravity, I numerically prove that the growth of boson stars inside halos slows down and saturates as has been previously conjectured, and detail its conditions. Self-interactions are included using the Gross-Pitaevskii-Poisson equations. We find that in the case of strong attractive self-interactions the boson stars can become unstable and collapse, in agreement with previous stationary computations. At even stronger coupling, the condensate fragments. Repulsive self-interactions, as expected, promote boson star formation, and lead to solutions with larger radii. Lastly, I simulate the formation of vortices during the merger of boson stars with gravity and find that weak attractive self-interaction can be ignored in this process.2021-11-1

Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation

Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing(NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list's ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality.Comment: Accepted to CONLL 201

Neural Machine Translation with Word Predictions

In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence.These vectors are generated by parameters which are updated by back-propagation of translation errors through time. We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English and German-English machine translation tasks show BLEU improvements by 4.53 and 1.3, respectivelyComment: Accepted at EMNLP201