Sentiment Analysis Sample

Data can be classified into three distinct categories: structured, unstructured, and semi-structured. It is important to understand these classifications and the challenges they present.

In the paper Nguyen et al \cite{Micro:Ng}, the model makes use of the Micro-reviews\cite{Micro:Ng} generated by the users through various social media sites about any particular entity. Micro-Reviews \cite{Micro:Ng} are the reviews that are not too long , easy to comprehend and also considered as the most appropriate feedback of the customer. But it is starting to get complicated as the number of Micro-Reviews \cite{Micro:Ng} are increasing and is hard to go through several thousands of the user reviews to find the best review suitable to the user preferences. In order to overcome this, these reviews are categorised in to either positive or negative feedbacks. Then this Micro-Reviews \cite{Micro:Ng} are associated and

Opinion mining (also known as sentiment analysis) focuses mainly on two things, first identifying whether the text is subjective or objective and then determining the polarity of the subject [3]. Opinion words in the text mostly contribute towards the subjectivity of the text. These opinion words together inform about the emotional context of the text. Objective words contribute towards the factual construct of the text. There are three most common approaches used for determining the sentimental content of a text

Document opinion analysis is about classifying the overall document that have sentiment words expressed by the authors. The task is to determine whether a document is positive, negative or neutral (Wawre & Deshmukh). Document level categorization attempts to classify sentiments in web forum postings, blogs, movie and news

In a style like traditional programming, calculations can happen in parallel on numerous machines in the Map stage. A similar thing likewise applies to the Reduce stage, with the goal that MapReduce applications can be greatly parallelized on a Cluster of machines.

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

My key responsibilities at Genesis Software Solutions Inc., as marketing analyst were to analyze the pros and cons of launching a new marketing campaign proposed by the company. To identify market opportunities and company’s needs I first met with the subject matter experts and understood all the requirements. With the knowledge transferred from the subject matter experts, I evaluated the scope of market penetration for the company. I also analyzed Return on investment. For this, company uses google analytics tools, to look at the market metrics and scope analysis. My internship at the company helped me to get familiarized with the usage of all these tools and how to generate and understand reports.

The purpose of this report is to give thorough insight on the IBM Watson Analytics tool. This report will provide background information on the tool, a description of the tool, describe the tool features and usage, discuss the shortcomings and criticism of the tool, and conclude with a summary of the product.

Unique Data Collection and Analysis Company is a business organization that provides its customers with unique methods and post-modern technologies of data collection and analysis since its inception in 2014. The company was founded in Chicago by Jack and Mary and currently has 20 employees. The company conducts market surveys on various products and services on behalf of its customers using web analytics and later combines it with operational systems data for accuracy in response to the market trends in the entire city and environs. The uniqueness, authenticity and easy-to-understand information drawn from numerous data sets and sources have continuously helped the customers to make informed managerial decisions and plans that have

An OLAP system provides facilities for summarization and aggregation, and stores and manages information at different levels of granularity. It manages large amounts of historical data. These features make the data easier to use in informed decision making.

The approach used in this thesis is inspired by Bollen et al’s strategy [12], with a step taken forward to implement PageRank algorithm to increase the accuracy of results and use of different sentiment analysis techniques than the techniques used by him. In 2010, Bollen used Twitter data for finding the predictability of Twitter sentiments on stock market with high accuracy. He proposed a method for prediction of the changes in the stock market price based on the mood of people on Twitter.

Opinionated text has created a new area in text analysis. Traditionally fact- and information-centric view of text is expanded to enable sentiment-aware applications. Now a day, opinion or sentiment extraction is very important task for both business and academic world. A producer would want to know what people say about its products on popular site (Aciar et al.2007). A corporation would be concerned about the review of product given by user and accordingly monitor the effectiveness of its advertising campaigns. Therefore, sentiment analysis is popular stream, which extracts sentiments and analyze it (Bing Liu, 2010; Bermingham et al. 2009; Chen, 2008; Cummins et al.2010).

Abstract- This survey reviews the recent progress in the field of sentiment analysis with the focus on available datasets and sentiment analysis techniques. Since many exhaustive surveys on sentiment analysis of text input are available, this survey briefly summarizes text analysis techniques and focuses on the analysis of audio, video and multimodal input. This survey also describes different available datasets. In most of the work datasets are prepared as per specific research requirements. This survey also discusses methods used to prepare such datasets. This survey will be helpful for beginners to obtain an overview of available datasets, methods to prepare datasets sentiment analysis techniques, and challenges in this area.

Designers require to compromise between optimizing query performance and maximizing query flexibility. Columnar databases use less disk space and are more efficient in their I/O demands than records-based data warehouses but force their own compromise between optimizing for new record insertion versus data selection and retrieval. The current paper gives an idea about advantages and disadvantages of Row and Column Oriented Database concepts. The row by row approach keeps information about an entity together. In the student example above, it will store all information about the first student and then all information about the second student and so on. The column by column approach keeps all attribute information together: all of the student name will be stored consecutively and then all of the student email, etc Both approaches are well designed and typically a choice is made based on performance expectation if the expected workload is based on entity (e.g., find a student, add a student etc) then row by row approach is preferable because all information regarding a particular student is stored together but if the expected workload tends to read per query only few attributed from many records (e.g., a query that finds most common email address domain), then column by column storage is preferable since irrelevant attributes for a particular query do not have to be access. The row by row approach is writing

Social opinion has been analysed using sentiment analysis (SA). This is basically a natural language processing (NLP) application that uses computational linguistics and text mining to identify text sentiments as positive, negative and neutral. This technique is known as emotional polarity analysis which is related to text mining field, opinion mining and review mining. In addition, to calculate sentiment score, the sentiment acquired from the text is compared to a dictionary in order to determine the strength of that sentiment. Studies on sentiment analysis focus on text written in English such as sentiment lexicons while applying this to other languages will cause domain adaptation problem [12]. Traditional text classification is different from sentiment classification. Traditional text classification refers to pre-defined class to determine a document’s category, and it gauges the theme of the text itself, while the main aim of the latter is to determine the attitudes and opinion through mining and analysing user interest or other subjective information [13]. Studies in sentiment analysis have found that pre-processing the data is the procedure of cleaning and adapting.