Extracting Attributes from Product Title and Image
This is a National (Singapore) Data Science Challenge organised by Shopee hosted on Kaggle. In the advanced category, the tasks is to extract a list of attributes from each product listing given product title and the accompanied image (a text and a image input). Training sets and full instructions are available in the Kaggle link. This is a short attempt of the problem which include the basic data exploration, data cleaning, feature extraction and classification.
Basic Data Exploration
While the project requirement have 3 main product categories, Beauty, Mobile, & Fashion, I will just focus on the Mobile data set. The two other categories will follow the same approach. For the mobile data set, the requirement is to extract the following attributes such as Brand, Phone Model, Camera, Phone Screen Size, Color Family. A brief exploration of the training data set observed.
- Only title (text) & image (pic) available to predict the several attributes
of the product.
- The attributes are already label-encoded.
- There are a lot of missing values particularly like Network Connections etc have more than 80% of data missing. This is quite expected as sellers unlikely to put some of these more obscured attributes in the title description while attributes like Brand and Model should have less missing data.
From seller’s perspective, seller will try to include as much information as possible in a
concise manner especially attributes like brands, models etc to make their posting relevant to search and stand out to the buyers. Using only image to extract attributes such as Brand and model might be difficult especially for mobile category where it is difficult to differentiate from pic even with human eye.
From the exploration, I planned the following steps.
- Using title (text) as main classification input and ignore images.
- Train and predict each attribute at a time.
Basic Data and Text Cleaning
There are some attributes Network Connections, Warranty Period which have large proportion of missing data. However, those attributes have majority of the observations having a certain attribute. In this case, those missing values are assigned with the mode of the training population (e.g. it is likely for Network Connections , most phones should be 4G etc). The attributes are also converted to integer for training purpose.
For the title, before extracting the numeric features, we perform cleaning on the data set. Since most users would highlight the most important feature in the product tile to make their product stand out and relevant, they would generally have omitted most of the stop words, most punctuation. and white spaces Hence for this data set, I will try minimal cleaning: change the title to lowercase and remove special characters. This can reduce a significant amount of time in text cleaning especially for large data set.
Data Cleaning and pipelines
For the advanced data extraction, I chose the Bag-Of-Word (BOW) model to generate the features from the text columns. In the BOW model, I use TF-IDF approach which computes the weighted frequency of each word in each title. For classification, SVM is chosen as the classifier. Pipe-lining makes it easy to streamline the whole text processing and attributes classification making it run on all the different attributes.
Below is the complete code running from extraction, cleaning to classification.
This is the starting point of the project and take only a few lines of code to get it up and running for quick analysis. I will improve the existing code by incorporate gridsearch for hyperparameters and expanding on the pipelines and features in the subsequent posts.