Fluent Essays

Naive Bayes LearningText Classification with Naive Bayes In this assignment, you will implement the Naive Bayes classification method and use it for sentiment classification of customer reviews. Write a report containing your answers, including the visualizations. Submit your report and your Python code/notebook. Preliminaries Read up the Naive Bayes classifier: how to compute apply the Naive Bayes rule, and how to estimate the probabilities you need. If you wish, you may also have a look at the following classic paper: · Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan :  Thumbs up? Sentiment Classification using Machine Learning Techniques . In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002). The dataset we are using was originally created for the experiments described in the following paper. The research described here addresses the problem of domain adaptation, such as adapting a classifier of book reviews to work with camera reviews. · John Blitzer, Mark Dredze, and Fernando Pereira:  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification.  In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL 2007). Preparatory remarks Frequency-counting in Python. The built-in data structure called Counter is a special type of Python dictionary that is adapted for computing frequencies. In the following example, we compute the frequencies of words in a collection of two short documents. We use Counter in three different ways, but the end results are the same (freqs1, freqs2, and freqs3 are identical at the end). The Counter will not give a KeyError if you look for a word that you didnt see before. from collections import Counter example_documents = [the first document.split(), the second document.split()] freqs1 = Counter() for doc in example_documents: for w in doc: freqs1[w] += 1 freqs2 = Counter() for doc in example_documents: freqs2.update(doc) freqs3 = Counter(w for doc in example_documents for w in doc) print(freqs1) print(freqs1[the]) print(freqs1[neverseen]) Logarithmic probabilities. If you multiply many small probabilities you may run into problems with numeric precision: the probability becomes zero. To handle this problem, I recommend that you compute the logarithms of the probabilities instead of the probabilities. To compute the logarithm in Python, use the function log in the numpy library. The logarithms have the mathematical property that np.log(P1 * P2) = np.log(P1) + np.log(P2). So if you use log probabilities, all multiplications (for instance, in the Naive Bayes probability formula) will be replaced by sums. If youd like to come back from log probabilities to normal probabilities, you can apply the exponential function, which is the inverse of the logarithm: prob = np.exp(logprob). (However, if the log probability is too small, exp will just return zero.) Reading the review music neg 241.txt i bought this album because i loved the title song . it s such a great song , how bad can the rest of the album be , right ? well , the rest of the songs are just filler and are nt worth the money i paid for this . it s either shameless bubblegum or oversentimentalized depressing tripe . kenny chesney is a popular artist and as a result he is in the cookie cutter category of the nashville music scene . he s gotta pump out the albums so the record company can keep lining their pockets while the suckers out there keep buying this garbage to perpetuate more garbage coming out of that town . i ll get down off my soapbox now . but country music really needs to get back to it s roots and stop this pop nonsense . what country music really is and what it is considered to be by mainstream are two different things . music neg 544.txt i was misled and thought i was buying the entire cd and it contains one song books neg 729.txt i have introduced many of my ell , high school students to lois lowery and the depth of her characters . she is a brilliant writer and capable of inspiring fierce passion in her readers as they encounter shocking details of her utopian worlds . i was anxious to read this companion novel and had planned to share it with my class this january . although the series is written for 6th graders and older , this book s simplicity , in its message , language and writing style will inspire no one . i am sadly disappointed books pos 278.txt anything you purchase in the left behind series is an excellent read . these books are great and very close to the bible . i have the entire set . amazon is a great shopping site and they ship fast . i would recommend these to any christian wanting to know about what to expect during the return of christ ! they are fiction but still makes a good point dvd pos 840.txt i loved these movies , and i cant wiat for the third one ! very funny , not suitable for chilren camera neg 715.txt in my experience , this camera takes great pictures , but the zoom lens is so delicate that it breaks easily . it stops extending and just makes a clicking noise . this happened to me when the camera accidentally turned on while in my pants pocket rendering the camera unusable . my brother and two of my friends experienced a similar problem with their ex-s500 after they accidentally dropped the camera while the lens was extended . i would recommend looking into another camera that is not so delicate books neg 120.txt first off i d like to tell you that this is only the second star wars book i ve ever read , jedi search being the first . i actually liked the first book , it drew upon the star wars lore well and was a decent read . however , this book made me groan in so many places that i felt almost compelled to create an account here to have a little rant about it ( be warned , this may contain a few spoilers ) . thusly : how much of the book was wasted with overly long descriptions ? it remin