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

Recombinant

The hybrid texts (poems and prose) in the following dissertation investigate female and genderqueer lineage in the context of labor smuggling and trafficking. In this book-length project, I examine the challenges of communal memory by juxtaposing voices from Asian, African and indigenous communities in the Americas. Set in a speculative future, these voices simultaneously inhabit their own spaces and share pathways, a theme developed through manipulation of white space on the page. The narrative speculates about the origins of M. Lao, a snakehead matriarch who has created a business empire from a fictional edu-tainment park, CoolieWorld, which traffics in the history of coolie labor. In the narrative, M. Lao is forced to confront her troubled relationship to her gender-non-conforming child who has disappeared as she considers her own history of migration, trauma, survival, self-invention and complicity in the trafficking of migrants. These writings force voices from various communities to interact with each other through the poems\u27 experimental graphic and representational practices. Rajagopalan Radhakrishnan asserts that diasporan realities do show up the poverty of conventional modes of representation with their insistence on single-rooted, non-traveling, natural origins. But this calls for multi-directional, heterogeneous modes of representation. By drawing on Radhakrishnan\u27s ideas, I create a diasporic poetics that contains multiple voices within a single space on the page. Poems that attempt to make sense of historical remnant share space with M. Lao\u27s fragmented narrative. I also blend historical incidents such as the 1899 anti-Chinese Milwaukee riots with the speculative realm of Coolie World, and in doing so think about how a city renegotiates its identity during long periods of constant redevelopment. To this end, I utilize historical artifacts including photographs; newspaper articles; maps; city directory listings; and records of immigration, birth and death, as well as scholarly research and archaeological records. These kinds of materials contain the shared memory of a community, and by juxtaposing, re-mixing, re-combining and erasing these found texts, recombinant examines both the erasure and reconstruction of community history

An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition

This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in- and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon dependent Value-at-Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales.Comment: To Appear: Journal of Futures Market

Gear ratio optimization of one- and two-speed transmissions for fully-electric vehicles

Currently, electric vehicles use a transmission with a single-speed fixed gear ratio optimized for performance. This work features the development of a framework to optimize the gear ratio for a given vehicle design, motor efficiency map, motor noise spectrum, and transmission noise spectrum. The framework provides a methodology for designing the optimization problem to be solved using derivative-free optimization methods. As electric vehicles become increasingly prevalent in the global automotive market, the need to develop multi-speed transmissions to provide a competitive edge against other electric vehicles. This work also explores the optimization of gear ratios for a two-speed transmission and identifies a corresponding switching point to switch between the two ratios. Using the same methodology, the optimization problem is formatted such that derivative-free optimization methods can be used to solve the optimization problem quickly and efficiently. Utilizing the framework developed, the program was able to identify an optimal gear ratio for a single-speed transmission in about half the time it took a brute-force search method to the same. The two-speed optimization process completed about 50 times faster than the brute force search. In both cases, the result returned by the optimization process was compared against the brute force search result to confirm global optimality. The single-speed gear ratio optimization, the two-speed gear ratio optimization, and the two-speed switching point optimization reliably located a global optimum.Mechanical Engineerin