基于主题的分类和确定创业公司的全球趋势
Ivan Savin1,2, Kristina Chukavina2, Andrey Pushkarev2
1Institute of Environmental Science and Technology, Universitat Autònoma de Barcelona, Barcelona, Spain.
概括
创业公司的趋势显示,他们正在转向数据分析和社交平台,而可持续农业和制药等重点领域的区域差异很大. 这项分析为超过25万家公司提供了新的,公正的分类.
科学领域:
- 商业和经济学 商业和经济学
- 数据科学数据科学数据科学
- 社会学 社会学 社会学
背景情况:
- 了解新兴市场领导者对于预测全球经济趋势至关重要.
- 在Crunchbase数据库中,有大量关于创业公司的文字描述.
研究的目的:
- 通过主题建模,开发一种新的,不偏见的创业公司分类.
- 分析2009-2019年创业主题的演变和区域分布.
主要方法:
- 主题建模应用于超过25万家初创企业的文字描述.
- 分析创业公司成立年份和地理位置.
- 对话题同时出现和吸引投资的量化.
主要成果:
- 创建了一个由38个主题组成的创业公司新分类.
- 在数据分析,社交平台,财务转账和时间管理初创企业中观察到越来越多的趋势.
- 移动游戏,在线新闻,社交网络和法律/专业服务的下降趋势.
- 主题集中度的区域差异很大 (例如,南美/非洲的可持续农业,北美/欧洲的制药).
结论:
- 该研究提供了一个动态的,数据驱动的了解初创公司的演变和区域专业化.
- 新的分类系统与现有数据库相比,提供了更好的洞察力.
- 调查结果强调了创业生态系统中创新和投资重点的关键转变.
相关概念视频
Classification of Systems-I
184
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
184
Classification of Systems-II
144
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
144
Aggregates Classification
317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
Classification of Signals
455
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
455
Types of Global Positioning System Surveys
55
GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
55
How Data are Classified: Categorical Data
32.6K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
32.6K


