Memory Standardization Meliton Padilla

Overview Introduction Related work Methodology Contribution Questions

Abstract Model the change of memory requirements for cell phones Introduction Methodology Related work Contribution Questions

Todays standards Introduction Methodology Related work Contribution Questions

Potential Issues Original approach Noise from multiple posts Not enough text to generate data Limited amount of data access Introduction Methodology Related work Contribution Questions

Product reviews Benefits Less noise Subject originated Large sample sizes Introduction Methodology Related work 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 Introduction Methodology Related work Contribution Questions

Related work Introduction Methodology Related work Contribution Questions

Key attributes Compactness Representativeness Readability 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 Introduction Methodology Related work 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 Introduction Methodology Related work Contribution Questions

Example Introduction Methodology Related work 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 Introduction Methodology Related work 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 Introduction Methodology Related work Contribution Questions

Example Readability scores of phrases Introduction Methodology Related work Contribution Questions

Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis 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 Introduction Methodology Related work Contribution Questions

Example Each summary should produce Introduction Methodology Related work 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 Introduction Methodology Related work Contribution Questions

Solutions Wordnet Part-of-Speech Tagging (POS) 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 Introduction Methodology Related work Contribution Questions

Example Using Wordnet to create a positive/negative approach a bipolar cluster Introduction Methodology Related work Contribution Questions

Methodology Introduction Methodology Related work 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 Introduction Methodology Related work Contribution Questions

Processing techniques Product reviews and previous data sets Introduction Methodology Related work Contribution Questions

Processing techniques Data is filtered Introduction Methodology Related work 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 Introduction Methodology Related work 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 Introduction Methodology Related work Contribution Questions

Contribution Being able to provide a way for consumers or manufacturers an easy method to decide on the memory required Introduction Methodology Related work 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