The objective is to provide crisp information about possibilities in Text analytics and how it can be leveraged in business scenarios

目的是提供有关文本分析中的可能性以及如何在业务场景中利用它的清晰信息。

“I got to work on this 500 page of tender, prepare a report in a day. How am I going to do it as still the last one is not sorted out; its submission date has already reached …”

“我必须处理这500页的招标，每天准备一份报告。 我怎么办呢，因为最后一个还没有整理好； 它的提交日期已经到了……”

Presales Team member (Legal/Business)

售前团队成员(法律/业务)

“we are getting hundreds of reviews for our apps hosted in app store… it’s too much to do with part time resource to understand what’s happening there ….”

“我们在应用程序商店中托管的应用程序获得了数百条评论……与兼职资源无关，以了解那里发生的事情……”。

App Owner

应用所有者

“Can someone give me insights on posts we are getting on twitter, please make sure I get them on a daily basis ….”

“有人能给我关于我们在Twitter上发布的帖子的见解吗，请确保我每天都收到它们……”

Head, Digital Channels

头，数字频道

“Looks like we are missing some vital information in most of the warranty claims, I am getting a feeling that pre-defined data points are not giving enough information to get to the bottom of the reasons for so many frequent claims ….”

“看起来我们在大多数保修索赔中都缺少一些重要的信息，我感觉到预定义的数据点所提供的信息不足以使许多频繁索赔的原因跌至最低点……”。

Warranty Claims Manager

保修索赔经理

“Looks like this IT request is for password reset and similar to what David marked lastly for another one but am not sure ….”

“看起来这个IT请求是为了重置密码，类似于David最后为另一个请求标记的，但是不确定……。”

IT Service Desk Executive

IT服务台主管

All the above information from different channels holds qualitative information about existing customers as well prospects and the problems they are facing and hence an opportunity for improvement. The problems are pointing to not so easily available information hidden deep inside text. In some cases, problem gets augmented due to the size of text to be processed whereas for some its standardization of analysis which varies based on human skillset as well experience. Solutions to most of these problem statements are available or can be developed with implementation of Artificial intelligence (AI), powered by Machine Learning (ML) or (and) Deep Learning (DL).

者A从不同渠道上面的信息持有约以及前景和问题，他们所面对的，因此改善的时机现有客户的定性信息。 问题在于隐藏在文本内部的信息不那么容易获得。 在某些情况下，由于要处理的文本的大小，问题变得更加严重，而在某些情况下，其分析的标准化根据人类的技能水平和经验而有所不同。 这些问题陈述中的大多数解决方案都可用，也可以通过由机器学习(ML)或(和)深度学习(DL)支持的人工智能(AI)的实施来开发。

Solutions for above listed examples comes under Text analytics domain and can be clubbed under one of the three major categories, also will try to brief at higher level what different solutions are available. Detailed solution approach we plan to cover for each category in subsequent posts

上面列出的示例的解决方案属于“文本分析”领域，可以归类为三个主要类别之一，还将尝试在更高级别上简要介绍可用的不同解决方案。 我们计划在后续职位中针对每个类别涵盖详细的解决方案方法

Classification:

分类：

It’s all about labeling a series of words or sentences to a more concise & meaningful one. This has lots of applications in different industries starting from labeling a service desk request to categorize the indirect parts consumed in manufacturing plants for costs optimization.

这就是将一系列单词或句子标记为更简洁和有意义的单词或句子。 从标记服务台请求到对制造工厂中消耗的间接零件进行分类以优化成本，这在不同行业中有许多应用。

This is indeed the simplest type of text analytics from algorithmic perspective. It has become more mature over a period with the advent of machine learning algorithms used in tandem with NLP engines

从算法的角度来看，这确实是最简单的文本分析类型。 随着NLP引擎一起使用的机器学习算法的出现，它已经变得越来越成熟。

Sentiment Analysis:

情绪分析：

As mentioned in previously listed examples, Teams or Owner responsible for Apps as well social media channels are struggling to find out how is the customer experience post launch of new products or apps with the addition of new features; whether it’s getting good or bad or just ok. The information flow is almost 24 X 7 and may also go multilingual. Hence there is a greater need to have a system which can provide real time sentiment analysis of feedback received from customers by business from multiple social media platforms and possibly accommodate multiple languages.

如前面列出的示例中所述，负责应用程序以及社交媒体渠道的团队或所有者正在努力寻找发布新产品或应用程序并添加新功能后的客户体验。 无论是好是坏，还是好的。 信息流几乎是24 X 7，也可能会变成多语言。 因此，更加需要一种系统，该系统可以提供对企业从多个社交媒体平台从客户那里收到的反馈的实时情绪分析，并且可能包含多种语言。

All AI platforms provides out of box sentiment analysis as well there are many open source solutions available to get this done without any binding to any platform. All these are quite matured & efficient and powered by models developed using ML.

所有AI平台都提供开箱即用的情感分析，并且有许多开源解决方案可实现此目的，而无需与任何平台绑定。 所有这些都非常成熟，高效，并且使用ML开发的模型提供了支持。

Summarization or Text Insights:

总结或文本见解：

Online Feedbacks / Blogs / Posts

在线反馈/博客/帖子

What these social media platforms as well similar apps do with data captured by user in form of tweets or posts or online feedback? What’s special in information keyed in by users can help business do better? Answer lies in serving customers by offering personalized service to each customer or prospect by developing a better understanding about them with the help of their reviews, feedback, views etc.

这些社交媒体平台以及类似的应用程序如何处理用户以推文或帖子或在线反馈形式捕获的数据？ 用户输入的信息有什么特殊之处可以帮助企业做得更好？ 答案在于通过为每个客户或潜在客户提供个性化服务来服务客户，借助他们的评论，反馈，观点等对客户或客户进行更好的了解。

Legal Contract Documents Analysis

合法合同文件分析

There is another area where quite a lot of information in structured manner flows out between business partners, although the volume of information generated is nowhere near to end users but from business impact perspective, it is quite a significant one. Here we are talking about Legal Business contract documents exchange between business partners which have quite significant value in commercial perspective. Considering the real-world timeline expectations and overall resource availability in general, it could be quite stressful for team to process them considering any lapse can result in significant loss to business.

在另一个领域中，业务合作伙伴之间有大量信息以结构化方式流出，尽管生成的信息量离最终用户不远，但是从业务影响的角度来看，这是相当重要的。 在这里，我们谈论的是商业伙伴之间的合法商业合同文件交换，这在商业方面具有相当重要的价值。 一般而言，考虑到现实世界中对时间轴的期望和总体资源可用性，考虑到任何失误都可能导致重大业务损失，团队对其进行处理可能会带来很大压力。

Warranty Claims Reviews

保修索赔评论

Similarly, in case of warranty claims, a significant information gets lost between transformation of raw data to structured one by Service Agents or other manual means. Delving more into informal text provided by end user can provide more insights i.e. if there is any pattern or trend coming up, and can help in making more informed decisions

同样，在保修索赔的情况下，在通过服务代理或其他手动方式将原始数据转换为结构化数据之间，会丢失大量信息。 深入研究最终用户提供的非正式文本可以提供更多的见解，即是否有任何模式或趋势出现，并有助于做出更明智的决策

Text analytics with help of Deep Learning & NLP algorithms are handy in this scenario to provide summary or insights. Using them one can bring out insights of varied degree from the content (text).

在这种情况下，借助深度学习和NLP算法进行文本分析非常方便，可以提供摘要或见解。 使用它们可以从内容(文本)中获得不同程度的洞察力。