{"id":1106,"date":"2019-02-15T10:31:58","date_gmt":"2019-02-15T10:31:58","guid":{"rendered":"http:\/\/kusuaks7\/?p=711"},"modified":"2023-08-08T13:49:24","modified_gmt":"2023-08-08T13:49:24","slug":"suggestion-and-opinion-mining-from-qualitative-surveys","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/suggestion-and-opinion-mining-from-qualitative-surveys\/","title":{"rendered":"Suggestion and Opinion Mining from Qualitative Surveys"},"content":{"rendered":"
\n

Problem Definition and Motivation<\/h3>\n

For a long time, open-ended qualitative surveys were usually conducted within small focus groups and the survey responses were typically manually coded and interpreted. Today with the explosion of social media conversations and online reviews, the notion of an Always On<\/em> focus group is real, and brands have started using automated tools based on machine learning and NLP to understand customer sentiments, opinions, suggestions, and needs from the continuous stream of conversations.<\/p>\n

Recently, we were involved in analyzing traveler feedback on a newly opened airport in Asia. The airport had been operational for about 8 months, generating close to 300,000 qualitative comments in social media and email surveys. The dataset contained suggestions and opinions on various aspects of the airport, ranging from boarding procedures to airport amenities. Our task was to understand what aspects of the airport the travelers were most satisfied or dissatisfied with, and which ones needed improvement. The results of our analysis were then fed into an action plan to help improve the airport\u2019s operations.<\/p>\n

Our Approach<\/h3>\n

Essentially, we extracted a combination of Sentiments, Intent, Interest, and Suggestions from the surveys and grouped them across various airport specific attributes to obtain a deeper understanding of the issues.<\/p>\n

Some issues that we needed to consider while approaching this problem were:<\/p>\n

    \n
  1. Customers who provide suggestions are generally unhappy about a particular aspect being discussed. For example, in the case I hope the security is good next time I come here<\/em>, <\/strong>the customer actually suggests improvement in security which is an indication of the dissatisfaction<\/em> of the customer. Sentiment extraction that uses keyword-based features like positive and negative adjectives would not be able to identify suggestions properly and would probably mistake suggestion sentences to be positive expressions.<\/li>\n
  2. It is also essential to figure out the airport feature that the customer is talking about and to accordingly identify sentences expressing opinion about a particular aspect of the subject being discussed.<\/li>\n<\/ol>\n

    In our case, opinionated statements could be among the following types:<\/p>\n

      \n
    1. Positive Expressions \u2013 sentences talking about a feature in a positive context<\/li>\n
    2. Negative Expressions \u2013 sentences talking about a feature in a negative context<\/li>\n
    3. Suggestive Expressions \u2013 sentences expressing a desire for improvement \/ wishful expressions<\/li>\n<\/ol>\n

      So in our approach, we proceeded to identify the three types of expressions that customers used to express opinions about the airport. In our case, we assume that negative and suggestive expressions indicate a negative opinion. Another challenge in our task was to identify the aspects about which customers were expressing opinions. The task objective clearly mentions to capture opinions about a certain set of features (food and beverage, shopping, restrooms, etc.<\/strong>). Manually building a terminology on these aspects of the airport is a time consuming process and the terminology obtained may not be complete. Therefore, for our purposes, we used a topic modeling-based approach to build our terminology (more on this will be discussed in the later sections).<\/p>\n

      From our extracted terminology, we then proceeded to identifying suggestions and sentiment- expressing sentences for our gathered features, using the following approaches:<\/p>\n

      1.\u00a0Features Extraction<\/h3>\n

      We used a topic modeling algorithm to extract the various themes across various reviews. After convergence of the Gibbs Sampling iterations, we got the topics associated with each review. A topic is a multinomial distribution over words. This example topic (airport, restroom, wait, queue<\/strong>) signifies that the theme of a certain review is related to \u201cwaiting for the restroom.\u201d From the topics obtained we manually created a terminology of features. For an in-depth understanding of how topic models work, please refer to the excellent paper Latent Dirichlet Allocation<\/em><\/strong><\/a> by David Blei.<\/strong><\/p>\n

      2.\u00a0Suggestion Extraction<\/h3>\n

      Suggestion Extraction is complex task by itself and previous research work is not conclusive. For extracting suggestions, we used some of the approaches followed by Ramanand et.al <\/strong>in Wishful Thinking Finding suggestions and \u2018buy\u2019 wishes from product reviews<\/em><\/strong><\/a>.<\/strong> They mention the use of rule-based approaches to extract suggestive texts from corpora. By manually browsing through random reviews, we have observed that modal verbs are an important aspect of suggestive sentences. For example, use of modal verbs like would<\/em>, should<\/em> <\/strong>in phrases need to be considered when building rules for suggestion classification. Some rules which we utilized when designing our suggestion extractor are given below:<\/p>\n

      1.\u00a0<modal_verb><preference_verb><optional_window_size_of_3><positive_sentiment_words><\/strong><\/p>\n

      Some examples of these rules are \u201cwould be great\u201d <\/strong>and \u201ccould be really good\u201d<\/strong>.<\/p>\n

      <\/p>\n

      2.\u00a0<modal_verb><optional_window_size_3><auxillary_verb><\/strong><\/p>\n

      <\/p>\n

      Some examples of these rules are \u201cwould like\u201d <\/strong>and \u201cwill really love\u201d<\/strong>.<\/p>\n

      Also, some general rules were used from manual extraction like \u201cshould come with\u201d<\/strong> and \u201creally needs to\u201d<\/strong>.<\/p>\n

      Our suggestion extractor trained on these rules was used to extract suggestions from the reviews. Our vocabulary was then used to identify all the suggestions for a particular feature of the airport.<\/p>\n

      3.\u00a0Sentiment Extraction<\/h3>\n

      After filtering out suggestive snippets, we used our vocabulary to extract expressions conveying a positive and negative opinion from the review corpus. For the sentiment extraction, we used our in-house sentiment engine that has been trained with a wide range of rules. Some design features that were taken into account when analyzing sentiments are:<\/p>\n