Memory Standardization Meliton Padilla
Overview • Introduction • Related work • Methodology • Contribution • Questions
Abstract • Model the change of memory requirements for cell phones Related work Introduction Methodology Contribution Questions
Todays standards Related work Introduction Methodology Contribution Questions
Original approach • Potential Issues • Noise from multiple posts • Not enough text to generate data • Limited amount of data access Related work Introduction Methodology Contribution Questions
Product reviews Benefits • Less noise • Subject originated • Large sample sizes Related work Introduction Methodology Contribution Questions
Main goal • Extract feature specification from textual reviews • Target memory for multiple devices • Allow product review monitoring to inform when a change needs to be made Related work Introduction Methodology Contribution Questions
Related work Related work Introduction Methodology Contribution Questions
Ganesan, K., Zhai, C., & Viegas, E. (2012, April). Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. InProceedings of the 21st international conference on World Wide Web (pp. 869-878). ACM. Key attributes • Compactness • Summaries should use as few words as possible (between 2-5) • Representativeness • Summaries should reflect major opinions in text • Readability - Summaries should be fairly well formed Related work Introduction Methodology Contribution Questions
Micropinon • A set of short phrases expressing opinions on a specific topic or entity • Leading to a method of also creating reviews on character limited social sites Related work Introduction Methodology Contribution Questions
Example Related work Introduction Methodology Contribution Questions
Issues from textual anaylsis • Different types of grammar • Recreating a new sentence in order to capture original opinion (without using any original text) • How to tell the difference between a factual statement compared to an opinion Related work Introduction Methodology Contribution Questions
solution • Similarity scores: sim(mi,mj) • Measured with Jaccard similarity measure (or cosine) • Allows control redundancy of the same opinion • Readability scores: Sread(mi,mj) - Measure well form structure of phrases (Microsoft Web N-gram) • Representativeness scores: Srep(mi,mj) • Measure how well a phrase represents the opinion from original text • Captured by a pointwise mutual information (PMI) function Related work Introduction Methodology Contribution Questions
Example • Readability scores of phrases Related work Introduction Methodology Contribution Questions
Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval 2.1-2 (2008): 1-135. Key attributes • Generating feature-based summaries • Distinguishing positive and negative comments • Grouping the data together to make looking for features easier Related work Introduction Methodology Contribution Questions
Example • Each summary should produce Related work Introduction Methodology Contribution Questions
Issues • How to tell if a opinion is positive or negative • Natural language processing techniques • Assuring the feature chosen is relatable to the product and not repeated Related work Introduction Methodology Contribution Questions
Solutions • Wordnet • System that helps find opinion words and frequent features • Part-of-Speech Tagging (POS) • Frequency of nouns, verb, adjective, etc. (Nlprocessor linguistic parser) • Orientation identification for opinion words - Only positive and negative orientations Related work Introduction Methodology Contribution Questions
Example • Using Wordnet to create a positive/negative approach a bipolar cluster Related work Introduction Methodology Contribution Questions
Methodology Related work Introduction Methodology Contribution Questions
Key differences • Focus just on the memory features of a device • Include other electronic devices besides just cell phones, examples such as laptops, mp3s and cameras • Sample current and past reviews to see if a trend can be modeled from the data Related work Introduction Methodology Contribution Questions
Processing techniques • Product reviews and previous data sets Related work Introduction Methodology Contribution Questions
Processing techniques Data is filtered Related work Introduction Methodology Contribution Questions
Processing techniques Steps needed • Collect large amount of data (may be separated by product type) • Extract opinion sentences and sort into a positive/negative category • Keep count of the positive to negative ratio • Use a similarity technique to measure the sweet spot of minimum required memory, in order to have a good product Related work Introduction Methodology Contribution Questions
Processing techniques Potential issues • Getting current reviews from Amazon • Currently provided API to view a current URL review page for 24hours • Comparing different products based on memory capability's • Analyzing textual data Related work Introduction Methodology Contribution Questions
Contribution • Being able to provide a way for consumers or manufacturers an easy method to decide on the memory required Related work Introduction Methodology Contribution Questions
Questions?
References • [1] Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis.Foundations and trends in information retrieval, 2(1-2), 1-135. • [2] Ganesan, Kavita. "Micropinions vs. Micro-reviews." Text Mining, Analytics & More:. N.p., n.d. Web. 12 Oct. 2016. • [3] Ganesan, K., Zhai, C., & Viegas, E. (2012, April). Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. InProceedings of the 21st international conference on World Wide Web (pp. 869-878). ACM. • [4] Qadir, A. (2009, September). Detecting opinion sentences specific to product features in customer reviews using typed dependency relations. InProceedings of the Workshop on Events in Emerging Text Types (pp. 38-43). Association for Computational Linguistics
