Current year resolutions

I’m alive!
I want to write about books that I read this year. I focused on building a team, operating processes and managing somethings. Here my quick review of my reading list.
1. Build an A-Team: Play to Their Strengths and Lead Them Up the Learning Curve
Author: Whitney Johnson.
I found a cute summary of the book*

build-an-a-team-book-summary-cartoon

2. A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills
Author: Jay Wengrow.
This book has extremely understandable content about the complexity of data structures.
a-common-sense-guide-to-data-structures-and-algorithms-cover

3. The Manager’s Path: A Guide for Tech Leaders Navigating Growth and Change
Author: Camille Fournier.
This book is already one of my favorite books. It has a solid structure and a strong look at what you need to think about at every step in your career from the first job to a player of big leagues. If you were a technical one and your career moving to leadership roles, you will absolutely find key lessons to learn.
the-managers-path-cover

4. The Checklist Manifesto: How to Get Things Right
Author: Atul Gawande
This book is written by a medical doctor to lead the way to get things right. Also, the author presented a great variety of sample fields, such as medical processes and airplane operations.
the-checklist-manifesto-cover

5. Building Evolutionary Architectures: Support Constant Change
Authors: Neal Ford, Patrick Kua, and Rebecca Parsons
It shows the value in evolvable systems that fit today’s dynamic software landscape.
building-evolutionary-architectures-cover

I think it is enough for this post, I’ll make additions until the end of the year.

* Build an a team book summary

Algorithm comparison on a social platform

I used newspaper articles in my experiments until now. I decided to use texts which extracted from other platforms, so I collected texts from eksisozluk platform. Ekşisözlük is a kind of local Reddit. I tried to perform a comparison experiment by using Turkish gerunds as features.
Here my experiment components:

    Corpus: Eksisozluk dataset of 5 authors represented by nicknames, 100 texts for each author. Average word count is 461, 80% of the dataset is used as training data and 20% of the dataset is used as test data.
    Features: Features are Turkish gerunds. These words are derived from the verbs but used as nouns, adjectives, and adverbs in a sentence. I listed the most widely used verbs in Turkish, after that I derived gerunds by using gerund suffixes. Finally, I obtained 590 verbal nouns, 587 verbal adjectives and 916 verbal adverbs (with proper vowel versions).
    Algorithms: Algorithms are LinearSVM, Multi-Layer Perceptron (MLP), Naive Bayes (NB), k-Nearest Neighbor (kNN) and Decision Tree.

Now, the results are below.

    SVM


The performance of SVM with gerund frequencies as features is not satisfied, it classified just 3 of 5 authors with correct matching minimum 12 of 20 test documents.

    MLP


The performance of MLP with gerund features is slightly better than SVM. For example, it classified 4 of 5 authors with correct matching minimum 12 of 20 test documents.

    NB


The performance of NB is average and close to other results. For example, it classified 3 of 5 authors with correct matching minimum 12 of 20 test documents.

    Decision Tree


The performance of Decision tree is not enough, average F1-score is 0.39. It did not make satisfied correct matching.

    kNN


The performance of kNN not enough but slightly better than decision tree, average F1-score is 0.44. It classified only one of 5 authors with correct matching 16 of 20 test documents.

As a result, NB, kNN and decision tree are not suitable algorithms for this approach. SVM and MLP performed better than other algorithms.